Flooding analysis tool and method thereof

ABSTRACT

Described herein are various embodiments of computer-implemented methods, computing systems, and program products for analyzing a flood operation on a hydrocarbon reservoir. An embodiment of a computer implemented method of identifying conformance candidates is provided. The embodiment includes, for a first entity: solving an injection entity index to generate an injection entity index value, solving a production entity index to generate a production entity index value, evaluating an operation entity index that represents operation status to generate an operation entity index value, and combining the injection entity index value, the production entity index value, and the operation entity index value to generate a conformance problem index value for the first entity.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit under 35 USC 119 of U.S. ProvisionalPatent Application No. 62/040,909 with a filing date of Aug. 22, 2014.This application also claims benefit under 35 USC 119 of U.S.Provisional Patent Application No. 62/135,016 with a filing date of Mar.18, 2015. This application claims priority to and benefits from theforegoing, the disclosures of which are incorporated herein byreference.

This application is one of multiple non-provisional patent applicationsfiled on Aug. 21, 2015 with the title of FLOODING ANALYSIS TOOL ANDMETHOD THEREOF. All of these non-provisional patent applications arerelated, and all of their disclosures are incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates generally to producing hydrocarbons froma subterranean reservoir using a flood operation, and more specifically,this disclosure relates to analyzing the flood operation.

BACKGROUND

Different mechanisms are typically utilized to produce hydrocarbons,such as oil and gas, from a subterranean reservoir. Initially,hydrocarbons are driven from the reservoir to the surface by the naturaldifferential pressure between the reservoir and the bottomhole pressurewithin a wellbore. After the pressure stage, an artificial lift systemsuch as a sucker rod pump and an electrical submersible pump can beutilized to drive hydrocarbons to the surface. After the artificial liftstage, a flood operation can be utilized to drive hydrocarbons to thesurface. In a flooding or flood operation, displacing fluid such aswater, gas, surfactants, polymers, etc. is injected into the reservoirvia one or more injection wells, and the displacing fluid displaces orphysically sweeps the hydrocarbons towards one or more producing wellsto the surface.

Analysis of a flood operation is oftentimes a difficult task. Forexample, the subterranean reservoir can include various geologicalfeatures such as faults, naturally occurring fractures, different rocktypes, etc., and these geological features affect how the injectionwells and the production wells are linked in the subterranean reservoir.Indeed, a typical field can have hundreds of wells, and the wells can belinked in all sorts of ways, further complicating analysis of the floodoperation. Therefore, the industry is always searching for improvementsin analyzing a flood operation.

SUMMARY

Described herein are various embodiments of computer-implementedmethods, computing systems, and program products for analyzing a floodoperation on a hydrocarbon reservoir.

In one aspect, a computer implemented method of identifying conformancecandidates is provided. The method includes, for a first entity: solvingan injection entity index to generate an injection entity index value,solving a production entity index to generate a production entity indexvalue, evaluating an operation entity index that represents operationstatus to generate an operation entity index value; and combining theinjection entity index value, the production entity index value, and theoperation entity index value to generate a conformance problem indexvalue for the first entity. The injection entity index includesinjection efficiency, value of injected fluid, and pore volume injectedfor a period of time. The production entity index includes estimate ofremaining movable oil in place.

In yet another aspect, a computer implemented method of determining aconformance control treatment for a well of a hydrocarbon reservoir,where the well is in fluidic communication with a plurality of zones ofthe hydrocarbon reservoir, is provided. The method includes receivingdata for each zone of the plurality of zones, the received data to beused to determine a residence time distribution for each zone. Themethod also includes determining the residence time distribution foreach zone of the plurality of zones using the received data. The methodalso includes identifying at least one zone of the plurality of zones tobe treated with a conformance agent by comparing the determinedresidence time distributions of the zones and a residence timedistribution threshold. The method also includes recommending a firstconformance control treatment at breakthrough time of the slowestidentified zone for the at least one identified zone.

In yet another aspect, a computing system for identifying conformancecandidates is provided. The system includes at least one processor andat least one memory containing computer executable instructions, thatwhen executed by the at least one processor, cause the computing systemto perform a method. The method includes, for a first entity: solving aninjection entity index to generate an injection entity index value,solving a production entity index to generate a production entity indexvalue, evaluating an operation entity index that represents operationstatus to generate an operation entity index value; and combining theinjection entity index value, the production entity index value, and theoperation entity index value to generate a conformance problem indexvalue for the first entity. The injection entity index includesinjection efficiency, value of injected fluid, and pore volume injectedfor a period of time. The production entity index includes estimate ofremaining movable oil in place.

The above summary section is provided to introduce a selection ofconcepts in a simplified form that are further described below in thedetailed description section. The summary is not intended to identifykey features or essential features of the claimed subject matter, nor isit intended to be used to limit the scope of the claimed subject matter.Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the present disclosure will become betterunderstood with regard to the following description, claims andaccompanying drawings where:

FIG. 1 illustrates an embodiment of a hydrocarbon reservoir with aplurality of injection wells and a plurality of production wells for aflood operation.

FIG. 2A illustrates a computing system useable to analyze a floodoperation on a hydrocarbon reservoir.

FIG. 2B illustrates an embodiment of a data management system.

FIG. 2C illustrates an embodiment of an interactivity workflow that maybe executed by the computing system of FIG. 2A.

FIGS. 3-5 illustrate one embodiment of a computer implemented method toanalyze a flood operation.

FIG. 6 illustrates one embodiment of a method for determining theallowed well connections.

FIGS. 7-10 illustrate examples consistent with the embodiment of FIG. 6.

FIG. 11 is another embodiment of a method for determining allowed wellconnections.

FIG. 12 illustrates an example consistent with the embodiment of FIG.11.

FIG. 13 illustrates one example of a conformance control workflow.

FIG. 14 illustrates an embodiment of a computer implemented method ofanalyzing a flood operation on a hydrocarbon reservoir having heavy oil.

FIGS. 15-17 illustrate examples consistent with the embodiment of FIG.14.

FIG. 18 illustrates one embodiment of a computer implemented method ofidentifying at least one conformance candidate.

FIGS. 19A-19B illustrates one embodiment of a computer implementedmethod of determining a conformance control treatment for a well of ahydrocarbon reservoir, where the well is in fluidic communication with aplurality of zones of the hydrocarbon reservoir.

FIG. 20 illustrates one embodiment of a computer implemented method ofanalyzing at least a first flood operation and a second flood operationon a hydrocarbon reservoir having at least one production well and atleast one injection well.

FIGS. 21A-21C, 22A-22B, 23A-23G, 24A-24F illustrate examples consistentwith the embodiment of FIG. 20.

FIG. 25 illustrates one example of the sharp viscosity variation betweennear well bore areas to far well bore areas.

FIG. 26 illustrates one example of separating each injection well andproduction well pair of a polymer flood operation into three tanksincluding a near injection well tank, a near production well tank, and amiddle tank between the near injection well tank and the near productionwell tank.

FIG. 27 illustrates one embodiment of a computer implemented method ofanalyzing a polymer flood operation on a hydrocarbon reservoir having atleast one injection well and at least one production well

FIGS. 28A, 28B illustrate one embodiment of a computer implementedmethod for using producer centered (e.g., Voronoi) polygons to helpidentify infill drilling locations and an example thereof.

FIGS. 29A, 29B, 29C illustrate one embodiment of a computer implementedmethod for using polygons to choose between two infill candidates in twodifferent reservoirs and examples thereof.

FIGS. 30A, 30B, 30C illustrate one embodiment of a computer implementedmethod for using polygons (e.g., streamgrids) for pattern realignmentand examples thereof.

FIGS. 31A, 31B, 31C illustrate one embodiment of a computer implementedmethod for using polygons (e.g., streamgrid) allocation factors toinitiate CRM interwell connectivity and examples thereof.

FIG. 32A, 32B, 32C illustrate one embodiment of a computer implementedmethod for estimating maximal areal sweep by zone and by reservoir withthe populated polygons (e.g., streamgrids) and examples thereof.

FIG. 33 illustrates one embodiment of a computer implemented method ofdetermining a value of injected fluid for a flood operation on ahydrocarbon reservoir having at least one injection well and at leastone production well.

FIGS. 34, 35A-35E, 36A-36H, 37A-37I illustrate one embodiment of amethod for analyzing a flood operation for a hydrocarbon reservoirhaving a plurality of zones and examples thereof.

DETAILED DESCRIPTION

The various embodiments of computer implemented methods described hereinmay be used to analyze a flood operation on a subterranean reservoir.Furthermore, the various methods described herein may be used incombination or individually. For example, the methods involvingdetermining allowed well connections may be used in conjunction withdetermining the value of injected fluid (VOIF). As another example, themethods involving determining allowed well connections may be used inconjunction with CRM zonal. As another example, the methods involvingdetermining allowed well connections may be used in conjunction withanalyzing a first flood operation and a second flood operation.

Furthermore, there may be a corresponding apparatus (e.g., computingsystems) and/or program product for each computer implemented method.Indeed, those of ordinary skill in the art will understand that theinvention is not limited to the disclosed embodiments, and for example,computer implemented methods, apparatuses, and/or program products, aswell as claim language for the same, are in the scope of thisdisclosure.

For ease of understanding, various running examples will be utilizedthroughout this disclosure. Also, for ease of understanding, terminologysuch as A, B, C, or any combination thereof may include (i) A only. Theterminology A, B, C, or any combination thereof may include (ii) B only.The terminology A, B, C, or any combination thereof may include (iii) Conly. The terminology A, B, C, or any combination thereof may include(iv) A and B. The terminology A, B, C, or any combination thereof mayinclude (v) A and C. The terminology A, B, C, or any combination thereofmay include (vi) B and C. The terminology A, B, C, or any combinationthereof may include (vii) A, B, and C.

Furthermore, some terms are used interchangeably herein, such asproducer and production well, injector and injection well, zone leveland zonal level, etc.

Flood Operation

FIG. 1 schematically illustrates an embodiment of a hydrocarbonreservoir 20 with a plurality of injection wells (or injectors) and aplurality of production wells (or producers) for a flood operation. Thereservoir 20 can be practically any type of subterranean or subsurfaceformation in which hydrocarbons are stored, such as limestone, dolomite,oil shale, sandstone, any combination thereof, or other subsurfaceformation. The reservoir 20 can be located onshore, offshore, deepwater,or at another location.

The flood operation can inject practically any displacing fluid such aswater, brine or salt water, water alternating gas referred to as WAG,gas (e.g., carbon dioxide), steam, surfactant, polymer, any combinationthereof (e.g., combination of polymer and surfactant), or othermaterial. The flood operation may be performed on the reservoir 20 overa few years or decades. More than one flood operation may also beperformed on the reservoir 20. For example, a first flood operationduring a first time period may inject water into the reservoir 20 and asubsequent second flood operation during a second time period may injectpolymer (or combination of polymer and surfactant) in the reservoir 20,as described further herein.

The injection wells and the production wells may be placed atpractically any location that may facilitate hydrocarbon production fromthe reservoir 20. For example, some of the wells may be placed inlocations that form a pattern. The pattern may be practically anypattern that can be used in a flood operation, such as a two spotpattern, a three spot pattern, a four spot pattern, a skewed four spotpattern, a five spot pattern, a seven spot pattern, an inverted sevenspot pattern, a nine spot pattern, an inverted nine spot pattern, adirect line drive pattern, a staggered line drive pattern, a peripheralflood pattern, an irregular pattern (e.g., an irregular five spotpattern), any combination thereof, etc. The reservoir 20 may alsoinclude different patterns and no-pattern well configurations.

At the well level, the production wells 30, 34 and the injection well 32are drilled and completed in the reservoir 20. The production wells orinjection wells can be completed in any manner (e.g., an openholecompletion, a cemented casing and/or liner completion, a gravel-packedcompletion, etc.) As illustrated in FIG. 1, completions 42, 44, 46, 50,52 provide fluid communication (e.g., via perforations) between theinjection well 32, the reservoir 20, and the production wells 30, 34.The production wells 30, 34 and the injection well 32 fluidly connectthe reservoir 20 to surface 40 of the subterranean reservoir 20. Thesurface 40 can be a ground surface as depicted in FIG. 1, a platformsurface in an offshore environment, etc.

Chokes or well control devices 54, 56, 60 are used to control the flowof fluid into and out of respective production wells 30, 34 andinjection well 32. The well control devices 54, 56, 60 also control thepressure profiles in the production wells 30, 34 and the injection well32. Although not shown, the production wells 30, 34 and the injectionwell 32 can fluidly connect with surface facilities (e.g., separatorssuch as oil/gas/water separators, gas compressors, storage tanks, pumps,gauges, pipelines, etc.) at the surface 40. The rate of flow of fluidsthrough the production wells 30, 34 and the injection well 32 can dependon the fluid handling capacities of the surface facilities at thesurface 40. The control devices 54, 56, 60 can be above surface and/orpositioned downhole.

During the flood operation, the displacing fluid injected by theinjection well 32 can drive the hydrocarbons of the reservoir 20 to theproduction well 30, the production well 34, or both depending on how thewells 30, 32, 34 are linked in the reservoir 20. The displacing fluidinjected by the injection well 32 can additionally drive thehydrocarbons of the reservoir 20 to a production well 62, a productionwell 64, or both depending on how the wells 30, 32, 34, 62, 64 arelinked in the reservoir 20 and the completions. Similarly, the fluidinjected by an injection well 66 can drive the hydrocarbons of thereservoir 20 to one or more of the production wells 30, 34, 62, 64depending on how the wells are linked in the reservoir 20, and so on foran injection well 68. At the zone or zonal level, the reservoir 20 caninclude a plurality of rock layers including hydrocarbon bearing stratasor zones 22, 24. This zonal level discussion will focus on wells 30, 32,34, but the discussion equally applies to wells 62, 64, 66, 68. Also,the reservoir 20 can include more zones than those illustrated in FIG.1.

The production wells 30, 34 and the injection well 32 extend into one ormore of the hydrocarbon bearing zones 22, 24 of the reservoir 20 suchthat the production wells 30, 34 and injection well 32 are in fluidcommunication with the hydrocarbon bearing zones 22, 24. The completions42, 44, 46, 50, 52 provide fluid communication (e.g., via perforations)between the injection well 32, the hydrocarbon bearing zones 22, 24, andthe production wells 30, 34. The production wells 30, 34 can receivedisplacing fluids (e.g., gas, oil, water, etc.) from the hydrocarbonbearing zones 22, 24 and the injection well 32 can inject fluid into thehydrocarbon bearing zones 22, 24. Also, the displacing fluid injectedinto one zone may flow into one or more different zones referred to ascrossflow. The production wells 30, 34 and the injection well 32 fluidlyconnect hydrocarbon bearing zones 22, 24 to the surface 40 of thesubterranean reservoir 20.

During the flood operation, the fluid injected by the injection well 32at the zone 22 can drive the hydrocarbons of the zone 22 of thereservoir 20 to the production well 30 at zone 22, the production well34 at zone 22, or both depending on how the wells 30, 32, 34 are linkedin the reservoir 20 at the zonal level. The fluid injected by theinjection well 32 at the zone 22 can additionally drive the hydrocarbonsof the zone 22 of the reservoir 20 to the production well 62 at zone 22(not shown), production well 64 at zone 22 (not shown), or bothdepending on how the wells 30, 32, 34, 62, 64 are linked in thereservoir 20 at the zonal level and the completions at the zonal level.Similarly, the fluid injected by the injection well 32 at zone 24 candrive the hydrocarbons of the reservoir 20 to one or more of theproduction wells 30, 34, 62, 64 depending on how the wells are linked inthe reservoir 20 at the zonal level and the completions at the zonallevel. For example, the production well 34 is not completed at zone 24and therefore hydrocarbons that flow along zone 24 towards theproduction well 34 may not be able to enter the production well 34 atzone 24. Similarly, the fluid injected by the injection well 66 at zone24 (not shown) can drive the hydrocarbons of the reservoir 20 to one ormore of the production wells 30, 34, 62, 64 depending on how the wellsare linked in the reservoir 20 at the zonal level and the completions atthe zonal level, and so on for the injection well 68.

Those of ordinary skill in the art will appreciate that FIG. 1 isprovided for context and various modifications are possible. Forexample, some embodiments can have fewer or more than the quantity ofwells illustrated in FIG. 1, as well as different patterns or welllocations. The production wells or the injection wells can also deviatefrom the illustrated vertical position such that in some embodiments,one or more wells can be a directional well, a horizontal well, or amultilateral well.

Hardware and Software

FIG. 2A illustrates a computing system 200 useable to analyze a floodoperation on a subterranean reservoir. The computing system 200 can, inexample embodiments, be communicatively connected to systems providingdata such as field data 222 and/or systems for further processing orinterpreting the field data 222 as described herein. The field data 222can include practically any data related to a field or componentsthereof. Examples of the field data 222 are discussed in connection withFIGS. 4-5. In some embodiments, the field data 222 can include non-fielddata, such as manufacturer data about a displacing fluid or materialthereof (e.g., manufacturer data about a polymer), manufacturer dataabout a conformance agent, manufacturer data on equipment tolerances,etc. In general, the computing system 200 includes at least oneprocessor 205 communicatively connected to at least one memory 204 viaat least one data bus 206. The processor 205 can be any of a variety oftypes of programmable circuits capable of executing computer-readableinstructions to perform various tasks, such as mathematical andcommunication tasks.

The memory 204 can include any of a variety of memory devices, such asusing various types of computer-readable or computer storage media. Acomputer storage medium or computer-readable medium may be any mediumthat can contain or store the program for use by or in connection withthe instruction execution system, apparatus, or device. By way ofexample, computer storage media may include dynamic random access memory(DRAM) or variants thereof, solid state memory, read-only memory (ROM),electrically-erasable programmable ROM, optical discs (e.g., CD-ROMs,DVDs, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.),magnetic tapes, and other types of devices and/or articles ofmanufacture that store data. Computer storage media generally includesat least one or more tangible media or devices. Computer storage mediacan, in some embodiments, include embodiments including entirelynon-transitory components. In the embodiment shown, the memory 204 maystore a flooding analysis application 212, discussed in further detailbelow. The computing system 200 can also include a communicationinterface 208 configured to receive and transmit data, for example, thefield data 222. Additionally, a display 210 can be used for presenting agraphical display of the flooding analysis application 212 orapplications or components thereof, for displaying maps (e.g.,saturation and pressure maps such as from a petrotechnical mappingapplication or component 220), for displaying user interfaces, fordisplaying curves, for displaying graphs, for displaying plots, fordisplaying tables, etc.

In various embodiments, the flooding analysis application 212 includes acapacitance resistance modeling or CRM application or component 214, apolygon application or component 216, a zonal application or component218, the petrotechnical mapping application or component 220. Thepolygon application 216 may be a streamgrid application or component216. CRM will be discussed further in the context of FIG. 3. In someembodiments, for example, the flooding analysis application 212 (orapplications or components 214, 216, 218, 220 thereof) can include oneor more other application or component 221 and/or receive data from theone or more other application or component 221. The one or more otherapplication or component 221 can be a vendor product, a petrotechnicalapplication such as a petrotechnical 4D seismic processing applicationor component, etc. In some embodiments, the CRM application 214, thepolygon application 216, the zonal application 218, the petrotechnicalmapping application 220, and/or the one or more other application 221can be standalone and separate from the flooding analysis application212.

Moreover, some or all the applications 214, 216, 218, 220, 221 caninteract with each other (e.g., as illustrated in FIGS. 2A, 2C). Forexample, some or all of the applications 214, 216, 218, 220, 221 caninteract by sharing data directly or providing data to a repository(e.g., operational data store (ODS)) that is accessible to the otherapplications.

In one embodiment, the flooding analysis application 212 may beassociated with a solution, such as a flood or waterflood surveillance,analysis, & optimization solution. The flood surveillance, analysis, &optimization solution may be one of many solutions i-Field® programsolutions, such as (i) a real time reservoir management solution, (ii) awell reliability & optimization decision support center solution, (iii)an integrated operations center solution, (iv) a drilling & completionsdecision support center solution, (v) a logistics decision supportsolution, (vi) a machinery & power support center solution, and (vii) areal time facilities optimization solution. An Upstream WorkflowTransformation (UWT) effort or other effort can be utilized toaccelerate the adoption and deployment of the solutions globally. Eachof the solutions may interact with each other. Each of these solutionsmay be associated with at least one application (e.g., the otherapplication 221) just like the flood surveillance, analysis, &optimization solution is associated with the flooding analysisapplication 212.

Each of these solutions may receive data from and/or send data to a datamanagement system referred to as data foundation or UWT data foundation.For example, the data management system may provide at least some of thefield data 222 for the flooding analysis application 212. Thus, in someembodiments, the field data 222 may represent the data managementsystem. The data management system may be centralized, and businessunits have access to all data for all business units or limited accessto the data based on permission. Alternatively, each business unit mayhave its own local data management system with data that is of interestto that business unit.

The data management system may help business units to source data forquicker, more efficient deployment of the solutions of interest. Forexample, some of the solutions may depend on a complex set of data fromdisparate IT systems across the business units, often with historiesspanning decades. Many business units may also have existing legacytechnology standards and IT systems in place with existing dependencieson service providers or vendors to support and maintain. Furthermore,some of the solutions may depend on an abundance of homeless data thatmay need to be collected, reviewed, and organized, and much of this datais decentralized across assets. Some of the solutions may also depend onunstructured data residing in non-traditional IT systems. The businessunits may differ widely in geographical locations, asset classes,business models, regulatory requirements, partnerships, and legalagreements that impact the ability to deploy solutions, especially wheredependencies on complex data types exist.

The data management system provides a standardized architecture toimprove efficiency in deployments of the solutions. For example, thedata management system provides a standardized architecture (e.g., basedon UWT target architecture and upstream data objectives), implementsconsistent data standards, implements data models based on internal andindustry standards, manages all forms of data, provides mature datagovernance, provides data accountability, and implements managedintegration to reuse data and enables business workflows. The targetarchitecture describes the preferred model for sourcing data frombusiness unit system of records, performing transformations to the datato support workflows, and moving this data into a format suitable foraccess by the appropriate solution. The data management system alsoincreases organizational capability to improve data quality for betterdecision making and decreased risk, for example, improving the qualityof the high value data in daily operations and making available highquality data to support growth rates in functions and business units.The data management system may also improve the ability to activelymonitor every barrel from business unit assets and may increase theability to proliferate business unit led innovations across otherbusiness units.

FIG. 2B illustrates an embodiment of a data management system 250. InFIG. 2B, “RM” stands for reservoir management, “D&C” stands for drillingand completions, “BU” stands for business unit, and “G&G” stands forgeology and geophysics. Of note, the data management system 250 includesa box 252 for data quality and business rule management, which mayrepresent business rules to improve data quality. For example, thebusiness rules can be applied to ensure certain data is not zero,certain data is numeric, certain data is text, certain data isalphanumeric, certain data is within a particular range, certain data isin the correct location, certain data is in the correct unit ofmeasurement, certain data is transformed, etc. The business rules canalso be based on correlations within data, such as existence of one ruledependent on existence of other data or range of other data. Also,business rules can search for heuristic trends within the data orboundary conditions such as date intervals between well tests. Asanother example, the business rules may ensure that wells are testedbased on predetermined assumptions or may apply practically any logictest The result of a business rule can be a monitoring condition or anaction applied programmatically. For example, the result of a businessrule may be a report or alert of failures, transposition of data basedon logical conditions, etc. The business rules may be generated based onfeedback from business units, based on requirements of the computingsystem or applications, any combination thereof, etc.

To create the data management system 250, existing business unit localdata foundation designs can be leveraged. Alternatively, existingbusiness local data foundation designs (e.g., functionally, technically,or by adding assets) can be extended. Alternatively, the system can bebuilt by partnering with an interested business unit when there is noexisting local data foundation capability to leverage prior designs.

The embodiment illustrated in FIG. 2C can be executed by the computingsystem 200 of FIG. 2A. FIG. 2C is discussed further in U.S. ProvisionalPatent Application No. 62/040,909, filed Aug. 22, 2014, title FLOODINGANALYSIS TOOL AND METHOD THEREOF, which is incorporated by reference inits entirety.

Those of ordinary skill in the art will appreciate that although certainterminology is used herein, such as the terms solution, application,component, etc., the invention is not limited to the exact embodimentsdisclosed herein. For example, embodiments consistent with thisdisclosure can be performed using computer executable instructions,computer executable code, modules, data structures, graphs, plots, maps,etc., and the embodiments are not limited to any specific arrangement inthis disclosure.

Referring generally to the systems and methods herein, and referring toin particular computing systems embodying the methods and systems of thepresent disclosure, it is noted that various computing systems can beused to perform the processes disclosed herein. For example, embodimentsof the disclosure may be practiced in various types of electricalcircuits comprising discrete electronic elements, packaged or integratedelectronic chips containing logic gates, a circuit utilizing amicroprocessor, or on a single chip containing electronic elements ormicroprocessors. Embodiments of the disclosure may also be practicedusing other technologies capable of performing logical operations suchas, for example, AND, OR, and NOT, including but not limited tomechanical, optical, fluidic, and quantum technologies. In addition,aspects of the methods described herein can be practiced within ageneral purpose computer or in any other circuits or systems.

Embodiments of the present disclosure can be implemented as a computerprocess (method), a computing system, or as an article of manufacture,such as a computer program product or computer readable media. The termcomputer readable media as used herein may include computer storagemedia. Computer storage media may include volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, routines, code, applications, programs,or program modules. Computer storage media may include RAM, ROM,electrically erasable read-only memory (EEPROM), flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other article ofmanufacture which can be used to store information and which can beaccessed by the computing system 200, above. Computer storage media doesnot include a carrier wave or other propagated or modulated data signal.In some embodiments, the computer storage media includes at least sometangible features; in many embodiments, the computer storage mediaincludes entirely non-transitory components.

Embodiments of the present disclosure can be implemented in hardwareonly, software only, or a combination of hardware and software.Furthermore, embodiments of the present disclosure can include at leastone server, at least on client device, a workstation, a distributedsetup, a mobile device, etc. depending on the implementation.

Embodiments of the present disclosure, for example, are described hereinwith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the disclosure. The functions/acts noted in the blocks may occur outof the order as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved. Embodiments may include fewer than ormore than the functionality/acts provided herein.

Capacitance Resistance Modeling (CRM)

Typically, CRM is run using only historical injection rates andproduction rates. CRM is built based on material balance and rooted insignal processing for nonlinear multivariate regression. With a ratevariation in an injector, CRM can capture interactions among productionwells and injection wells by matching the response in the productionrates. Typically, running CRM results in two sets of parameters, namely,interwell connectivity (F_(ij)) and response time (Tau or τ) perinjection well and production well pair. The interwell connectivityquantifies the support from one injection well to one production well,and the response time estimates the time a production well takes toresponse to a variation in the injection rate. Those parameters can bedetermined via history matching the production rates of all productionwells. CRM can have three forms: CRMT to represent the drainage volumeof the entire field, CRMP to represent the drainage volume of eachproduction well, and CRMIP to represent the drainage volume of eachproduction well and injection well pair.

Injection rates and production rates are typically used as input to CRM,as discussed in Weber, et al. “Improvements in Capacitance-ResistiveModeling and Optimization of Large Scale Reservoirs,” SPE 121299, 2009SPE Western Regional Meeting held in San Jose, Calif., USA, 24-26 Mar.2009, which is incorporated by reference in its entirety. The followingdocuments also discuss CRM, and each of these documents is incorporatedby reference in its entirety: (i) Sayarpour, et al. “The Use ofCapacitance-Resistive Models for Rapid Estimation of WaterfloodPerformance and Optimization”, SPE 110081, 2007 SPE Annual TechnicalConference and Exhibition held in Anaheim, Calif., USA, 11-14 Nov. 2007,(ii) Sayarpour, et al., “Field Applications of Capacitance ResistiveModels in Waterfloods”, SPE 114983-MS, 2008 SPE Annual TechnicalConference and Exhibition held in Denver, Colo., USA, 21-24 Sep. 2008,(iii) Sayarpour, et al., “Field Applications of Capacitance ResistiveModels in Waterfloods”, SPE 114983-PA, December 2009 SPE ReservoirEvaluation & Engineering, (iv) Sayarpour, et al., “Probabilistic HistoryMatching With the Capacitance-Resistance Model in Waterfloods: APrecursor to Numerical Modeling”, SPE 129604, 2010 SPE Improved OilRecovery Symposium held in Tulsa, Okla., USA, 24-28 Apr. 2010, and (v)Sayarpour, M., “Development and Application of Capacitance-ResistiveModels to Water/CO₂ Floods”, pages 1-236, available athttp://repositories.lib.utexas.edu/handle/2152/15357?show=full, whichare all incorporated by reference in their entireties.

Allowed Well Connections

FIGS. 3-12 illustrate embodiments of computer implemented methods ofanalyzing a flood operation on a hydrocarbon reservoir having at leastone production well and at least one injection well, and moreparticularly, embodiments that include determining allowed connectionsthat may be used as input to CRM. The embodiments illustrated in FIGS.3-12 can be executed by a computing system, such as the computing system200 of FIG. 2A or the CRM application 214 thereof. The allowed wellconnections may affect history matching and generation of the interwellconnectivities when CRM is run, which may lead to more accurate outputfrom CRM, such as more accurate response times and interwellconnectivities. The output from CRM and/or other data available afterexecuting the embodiments may be used as input to update or integratewith the polygon application 216, the zonal application 218, thepetrotechnical mapping application 220, the one or more otherapplication 221, the flooding analysis application 212, and/or otheritem (e.g., as illustrated in FIGS. 2A, 2C).

Referring to FIGS. 1-13, FIG. 3 illustrates one embodiment of a method300 that can be executed by a computing system, such as the computingsystem 200, to analyze a flood operation on the reservoir 20 or thelike. For simplicity, the discussion will first focus on the well level,and then turn to the zonal level.

At 302, field data is received. Receiving field data can includeacquiring, capturing, obtaining, requesting, providing, etc. Forexample, the field data 222 can be received from the wells 30, 32, 34,62, 64, 66, 68 of the reservoir 20, the well control devices 54, 56, 60,sensors, meters, transmitters, and other equipment (e.g., hydrophones,gauges, etc.), databases, ODS, the data management system 250 of FIG.2B, analysis at the surface 40 (e.g., laboratory analysis), datastreams, users, any combination thereof, etc. The field data 222 can bereceived in a raw state or in a non-raw state (e.g., errors are removedfrom the raw data). The field data 222 may include text, values,measurements, signals, etc. The field data 222 can be static or dynamic.The field data 222 can be real-time data, but does not have to bereal-time data. The field data 222 can be numeric, text, alphanumeric,etc. The field data 222 can be actual data from actual hydrocarbonproduction, or in some embodiments, the field data 222 can includesimulation data, synthetic data, estimates, forecasts, predictions, etc.The field data 222 may be streaming or non-streaming.

FIG. 4 elaborates on the field data 222 that can be received, with thestatic data illustrated in gray shading and the dynamic data illustratedwith no shading. Examples of field data 222 are illustrated as 402-420,however, the particular field data received for a particular reservoiralso depends on the particular field data that is actually gathered forthat particular reservoir. For example, if there is both tracer data and4D seismic data for the particular reservoir, then both of these can bereceived and used consistent with this disclosure. However, if there is4D seismic data, but not tracer data, for the particular reservoir, thenthe 4D seismic data can be received and used consistent with thisdisclosure. As such, FIG. 4 as well as FIG. 5 illustrate examples ofreceived field data and processing of the received field data, andshould not be considered a minimum and not be considered a maximum.

The field data 222 can include static geological data 402 such asporosity, permeability, fracture data (e.g., fracture architecture,fracture locations, etc.), fault data (e.g., fault architecture, faultlocations), rock information, etc. The geological data 402 can bedetermined by users (e.g., by examining core samples, outcrops, etc.),from models, from simulations, any combination thereof, etc.

The field data 222 can include static seismic data such as threedimensional (3D) seismic data 404. The 3D seismic data 404 can bepractically any geophysical data measured remotely. For example, the 3Dseismic data 404 can be recorded through the use of active seismicsources (e.g., air guns or vibrator units) and receivers (e.g.,hydrophones or geophones). The sources and receivers may be arranged inmany configurations, and a seismic survey is designed to optimize thesource and receiver configurations. Therefore, the 3D seismic data 404can include seismic surveys, can be recorded by sensors or receivers atthe surface 40, can be recorded by sensors or receivers in boreholes,any combination thereof, etc.

The field data 222 can include dynamic well production data (e.g.,hydrocarbon volumes produced data or production rate data), injectionrate data (or simply injection data), pressure data, and location data406. For example, the well production data can be received from flowmeters (e.g., multiphase flow meters) at the production wells 30, 34,62, 64, whereas the injection rate data can be received from flow metersat injections wells 32, 66, 68. The pressure data can be received frompressure sensors, as well as other downhole or surface equipment. Thelocation data can include x, y, and z coordinates, and can be receivedfrom sensors, GPS equipment, any combination thereof, etc. Of note, thewell production data, the injection rate data, the pressure data, andthe location data (or depth data) can be at the zonal level, forexample, from the zonal application 218 depending on the embodiment.

The field data 222 can include dynamic fluid production geochemistrydata 408. For example, the geochemistry data 408 of the produced fluidsfrom the production wells 30, 34, 62, 64 can provide the composition ofthe produced fluids, such as a listing of the different items in theproduced fluid as well as quantity measurements for the different itemsin the produced fluid. The geochemistry data 408 can be received fromequipment, from analysis at the surface 40, from laboratory analysis,any combination thereof, etc.

The field data 222 can include dynamic tracer data 410. A tracer is amaterial that can be injected with water or other flooding material intothe injection well 32 during the flood operation, and the tracer can bedetected after a period of time at one or more of the production wells30, 34, 62, 64 depending on how the wells are linked in the reservoir20. The tracer data 410 measures movement of the tracer as it travelsthrough the reservoir 20. The tracer data 410 can be received fromequipment, from analysis at the surface 40, from laboratory analysis,any combination thereof, etc.

The field data 222 can include dynamic log data 412, such as well logdata and production log data. The field data 222 can also includedynamic well test data 414. For example, well test data can include aproduction logging test (PLT), an injection logging test (ILT), etc.

The field data 222 can be dynamic seismic data, such as time lapseseismic data, also referred to as four dimensional (4D) seismic data416. For 4D seismic data 416, a baseline seismic survey is performed toobtain a baseline seismic dataset and subsequent monitoring seismicsurveys are performed to obtain one or more monitor seismic dataset(s).Differences between the baseline and the monitor seismic dataset(s) canbe analyzed to determine changes in the subsurface reservoir 20 affectedby the production and/or injection. The 3D seismic data 404 can be usedto determine the 4D seismic data 416.

The field data 222 can be polygon data 418. For example, the polygondata 418 may include polygons indicating geo-bodies, geologicalboundaries or structural boundaries such as fault or fractures from 3Dseismic and/or 4D seismic data, well patterns, etc. The polygons may beoverlapping, overlaying, etc. The polygon data 418 may be received fromthe polygon application or component 216.

The field data 222 can also include CRM output data 420. For example,CRM may be run a plurality of times and the CRM output can be the CRMoutput data 420.

Those of ordinary skill in the art will appreciate that the listing offield data 222 illustrated in FIG. 4 is not exhaustive. More field dataor different field data can be received in some embodiments. Forexample, as will be discussed hereinbelow, this disclosure expresslycontemplates receiving field data at the zonal level (e.g., field datasuch as production data and injection data at the zonal level from thezonal application 218).

Returning to FIG. 3, at 304, at least a portion of the received fielddata 222 can be processed. For example, some of the field data 222received at 302 can be utilized as-is, but at least a portion of thereceived field data 222 can be processed by a computer executed methodonly, a user only, or both a computer executed method and a user.Processing the received field data 222 can include analyzing,interpreting, generating additional data, generating maps, generatingcurves, generating tables, laboratory experiments, etc. Thus, at 304,the received field data 222 can be processed using computer algorithms,manual methodologies, interpretation (e.g., user interpretation),procedures, tests, equipment, etc. For example, the received field data22 can be processed in a manner known to those of ordinary skill in theart.

FIG. 5 elaborates on the processing of the received field data. At 502,geological boundaries and/or geobodies of the reservoir 20 can bedetermined from the seismic data (3D seismic data) 404. For example, apetrotechnical application 221 executing on the computing system 200 ofFIG. 2A can compute the geological boundaries and/or geobodies of thereservoir 20 from the 3D seismic data 404. The petrotechnicalapplication 221 can be separate from the flooding application 212 orpart of the flooding application 221 depending on the embodiment.Furthermore, the geobodies can be determined from the well log data at504 by the same petrotechnical application 221 or another application(e.g., another pertrotechnical application). At the zonal level,continuity of zones can also be determined from the well log data at504.

At 506, the geochemistry data analysis can be conducted on thegeochemistry data 408. For example, the geochemistry data can beanalyzed to determine salinity of produced fluids, determine the amountof water in the produced fluids, determine the various components of theproduced fluids (e.g., fingerprinting the oil or produced fluids), etc.The geochemistry data analysis can be conducted at 506 by the samepetrotechnical application 221 or another application (e.g., anotherpertrotechnical application).

At 508, tracer data analysis can be conducted on the tracer data 410.For example, the tracer data from the production wells 30, 34, 62, 64can be analyzed to determine how fluid flows through the reservoir 20and for estimating remaining hydrocarbons in the reservoir 20. Thetracer data analysis can be conducted at 508 by the same petrotechnicalapplication 221 or another application (e.g., another pertrotechnicalapplication).

At 510, pressure transient analysis can be conducted on the well testdata 414. For example, one or more pulse tests can be part of thepressure transient analysis. The tracer data analysis can be conductedat 510 by the petrotechnical application 221 or another application(e.g., another pertrotechnical application).

At 512, pressure and saturation can be determined from the time lapsedata (4D seismic data) 416. The pressure and saturation can be computedat 514 by the petrotechnical application 221 or another application(e.g., another pertrotechnical application). Furthermore, at 514, mapscan be generated and/or displayed using the determined pressure andsaturation by the petrotechnical mapping application 220 or anotherapplication (e.g., another pertrotechnical application). Thepetrotechnical mapping application 220 can be separate from the floodingapplication 212, separate from the petrotechnical application 221, apart of the petrotechnical application 221, or a part of the floodinganalysis application 212 depending on the particular embodiment. Thepetrotechnical mapping application 220 can be a remaining resourceapplication or component of the flooding analysis application 212, forexample, as in U.S. Non-Provisional patent application Ser. No.13/952,783, filed Jul. 29, 2013, title System and method for remainingresource mapping, which is incorporated by reference in its entirety.Each of the following papers is also incorporated by reference in itsentirety: (i) Dietrich, et al, “A Method for Determining Reservoir FluidSaturations Using Field Production Data”, SPE 3961, SPE-AIME 47^(th)Annual Fall Meeting held in San Antonio, Tex. on Oct. 8-11, 1972, and(ii) Gruy, H. J., “Graphing facilitates tracking water and gas influx”,Oil & Gas Journal, Mar. 26, 1990, which are both incorporated byreference in their entireties.

For example, maps of estimated change in pressure and water, oil, and/orgas saturation of the reservoir 20 can be generated using data drivenapproaches that combine observed 4D seismic data or signal with wellpressure and production/injection rate as explained further in U.S.Provisional Patent Application No. 62/081,968, filed Nov. 19, 2014,title SYSTEM AND METHOD FOR PRESSURE AND SATURATION ESTIMATION FROMTIME-LAPSE SEISMIC DATA, which is incorporated by reference in itsentirety. A copy of the U.S. Provisional Patent Application No.62/081,968 was included in U.S. Provisional Patent Application No.62/135,016, which is incorporated herein by reference in its entirety.Alternatively, or additionally, maps of estimated change in pressure andwater, oil, and/or gas saturation of the reservoir 20 can be generatedusing model driven approaches that use physics modeling to estimatechanges of pressure and saturation from 4D seismic data or signal.Alternatively, or additionally, maps of seismic attributes can becomputed from 4D seismic data or signal.

Indeed, the pressure and saturation, and maps thereof, can includequalitative visualization of cross sections and map views of 4D seismicamplitudes (e.g., by a computer executed method only, a user only, orboth a computer executed method and a user) and/or qualitativevisualization of cross sections and map views of seismic impedances frominversion of seismic data (e.g., by a computer executed method only, auser only, or both a computer executed method and a user). Of note,although maps can be generated and displayed to a user, in someembodiments, maps do not need to be generated and displayed to a user.

The various items 502-514 and 402-420 can be checked for consistency at516. For example, at least a portion of the various items 502-514 and402-420 can be reviewed for accuracy, contradictions, and overallconsistency by a computer executed method only, a user only, or both acomputer executed method and a user. The consistency interpretation at516 can be performed by computerized methods only, users only, or bothcomputerized methods and users depending on the embodiment. If anyinconsistencies are found, control can pass to 304 to continue tore-process as appropriate and as many times as appropriate. The loop anditerations can continue until a sufficient level of consistency isreached.

The various items 502-514 and 402-416, after the consistency check, canlead to results 518-528. For example, the results 518-528 can include(i) the faults and the fractures (518) from the geological data (402),(ii) the geological boundaries (520) from the 3D seismic data (502,404), (iii) the geobodies (522) from the 3D seismic data (502, 404)and/or the well log data (504, 412), (iv) the well production data,injection rate data, and the pressure date (524) from 406, (v) maps ofchange in pressure and saturation (526) from the 4D seismic data and/orthe pressure and saturation maps (514, 512, 416), (vi) polygons 517 fromthe polygon data 418, and/or (vii) interwell connectivities and responsetimes from the CRM output data 420. Moreover, the results includepreliminary well connections (528) or evidence thereof, for example,from the tracer data analysis (508, 410) and/or the pressure transientanalysis (510, 414). This listing of results is not exhaustive and otherresults can also be included.

Returning to 306, 308, 310 of FIG. 3, practically any available data atthis point (e.g., the received field data 402-416 and the processedfield data (or interpreted field data) 502-514 and 518-528), can be usedfor input to CRM and/or to determine allowed well connections at 308,310 and the allowed well connections are input to CRM. For example,injection rates and production rates can be used as input to CRM.Therefore, the well production data and the injection rate data 406 canbe used as input to CRM at 306.

However, consistent with this disclosure, input to CRM will also includeallowed well connections, not just the well production data and theinjected rate data 406. In a particular example, some but not all of thepreliminary well connections 528 will be allowed well connectionsdepending on the determination. The allowed well connections can bedetermined at 308 from at least a portion of available data at thispoint (e.g., the received field data 402-416 and the processed fielddata (or interpreted field data) 502-514 and 518-528 illustrated in FIG.5). For example, in some embodiments, time lapse seismic data (4Dseismic data), including interpreted time lapse seismic data(interpreted 4D seismic data) and any of 416, 512, 514, can be used asan input to CRM and/or to determine allowed well connections and theallowed well connection are input to CRM. Interpreted time lapse seismicdata (interpreted 4D seismic data) can be generated by, for example, atleast one of:

(a) maps of estimated change in pressure and water, oil, and/or gassaturation of the reservoir generated using data driven approaches thatcombine observed 4D seismic data or signal with well pressure andproduction/injection rate as explained further in U.S. ProvisionalPatent Application No. 62/081,968, filed Nov. 19, 2014, Chevron No.T-10040-P, title SYSTEM AND METHOD FOR PRESSURE AND SATURATIONESTIMATION FROM TIME-LAPSE SEISMIC DATA, which is incorporated byreference in its entirety—a copy of the U.S. Provisional PatentApplication No. 62/081,968 was included in U.S. Provisional PatentApplication No. 62/135,016, which is incorporated herein by reference inits entirety,

(b) maps of estimated change in pressure and water, oil, and/or gassaturation of the reservoir generated using model driven approaches thatuse physics modeling to estimate changes of pressure and saturation from4D seismic data or signal,

(c) maps of seismic attributes computed from 4D seismic data or signal,

(d) qualitative visualization of cross sections and map views of 4Dseismic amplitudes (e.g., by computerized methods, interpretation by auser, or both computerized methods and interpretation by a user), and/or

(e) qualitative visualization of cross sections and map views of seismicimpedances from inversion of seismic data (e.g., by computerizedmethods, interpretation by a user, or both computerized methods andinterpretation by a user).

Although maps can be generated and displayed to a user, in someembodiments, maps do not need to be generated and displayed to a user.Therefore, interpreted time lapse seismic data can be generated by thedata behind the maps via computerized methods, interpretation by a user,or both computerized methods and interpretation by a user. Nonetheless,the allowed well connections can be determined at 308 from at least aportion of available data at this point (e.g., the received field data402-416 and the processed field data (or interpreted field data) 502-514and 518-528 illustrated in FIG. 5). Moreover, any of this available datacan be used for input to CRM and/or to determine allowed wellconnections at 308, 310 and the allowed well connections are the inputto CRM. For example, in some embodiments, time lapse seismic data (4Dseismic data), including interpreted time lapse seismic data(interpreted 4D seismic data) and any of 416, 512, 514, can be used asan input to CRM and/or to determine allowed well connections and theallowed well connection are input to CRM. Moreover, in some embodiments,time lapse seismic data (4D seismic data), including interpreted timelapse seismic data (interpreted 4D seismic data) and any of 416, 512,514, along with other data (e.g., tracer data) can be used as an inputto CRM and/or to determine allowed well connections and the allowed wellconnection are input to CRM. In short, practically any of the dataavailable at this point can be used as input to CRM (e.g., constraintsto CRM) and/or to determined allowed well connections that will be inputto CRM (e.g., constraints to CRM).

FIG. 6 provides a method 600, which is an embodiment of a method fordetermining the allowed well connections. The method 600 can be executedby the CRM application 214. In some embodiments, the method 600 can beiterative, and allowed well connections can be determined for eachinjection well and production well pair. The method 600 can be executedfor multiple injection wells or executed per injection well. The datasetof allowed well connections may be modified (e.g., iteratively) based ona distance category, a static features category, a dynamic featurescategory, or any combination thereof. For example, the database ofallowed well connections may be modified as will be discussed below inconnection with 604, 606, 608. For ease of understanding, a runningexample for determining allowed connections will be discussed in thecontext of FIGS. 7-10. For simplicity, the running example will focus oninjection wells Inj-05 and Inj-08 and production wells Pro-01 throughPro-12. Bold and strikethrough are used to indicate the changes betweeniterations.

The method 600 can be accomplished by a computer executed method only, auser only, or both a computer executed method and a user. In someembodiments, the order of distance, then static features, and thendynamic features of the method 600 can also be changed such that thedynamic features are higher priority and used first, then the staticfeatures, then the distance. Moreover, within each category, there canalso be a priority (e.g., geobodies have a higher priority thangeological boundaries or permeability, or vice versa, within the staticfeature category) (e.g., 4D seismic data or the pressure and saturationfrom 4D seismic data has a higher priority than tracer data, or viceversa, within the dynamic features category). Thus, there can be apriority between categories (e.g., dynamic features, then staticfeatures, then distance) and/or a priority within a category (e.g.,geobodies have a higher priority than permeability within the staticfeature category or vice versa). Moreover, a sensitivity analysis can berun for each category and the allowed well connections per category canbe based on the sensitivity analysis per category.

In a computer method only implementation of method 600, the computermethod can rely on location data, boundaries, thresholds such as if theproduction well is within a number of feet or percentage outside of ageobody or saturation and pressure map boundary, and other data todetermine the allowed well connections. Furthermore, it can display thecorresponding diagrams/maps as in FIGS. 7-10, but it does not need todisplay the diagrams/maps. In some embodiments, a user can determinedthe allowed well connections as indicated in method 600, or revise theallowed well connections determined by a computer method onlyimplementation. For example, the user can do so if he or she wants touse more flexibility and expertise, and not thresholds, in decidingwhether a production well is connected or not to an injection well.

At 602, a dataset of allowed well connections can be established. Thevalues for the allowed well connections can be initially established byindicating that a well connection exists for each injection well andproduction well pair. For example, a value of 1, yes, or otherindication can be used to indicate a well connection between aninjection well and a production well. In the running example, at 602,the dataset of allow well connections may indicate that each injectionwell can potentially support each production well, in other words, thatthere is a well connection between every production well and eachinjection well, as illustrated below:

TABLE 1 Dataset of allowed well connections . . . Inj-05 . . . Inj-08Pro-01 1 1 Pro-02 1 1 Pro-03 1 1 Pro-04 1 1 Pro-05 1 1 Pro-06 1 1 Pro-071 1 Pro-08 1 1 Pro-09 1 1 Pro-10 1 1 Pro-11 1 1 Pro-12 1 1

At 604, the dataset of allowed well connections can be modified based ona distance category (e.g., inverse distance). For example, a value of 1for a particular injection well and production well pair is changed from1 to 0 to indicate that a well connection does not exist based ondistance for that particular injection well and production well pair.Alternatively, the value is kept at 1 if the well connection existsbased on the distance category. A priority between categories may bereceived from a user, for example, and the priority may indicate thatthe distance category is first. Similarly, a priority within thedistance features category may be used and received from a user. A usermay designate the data that will be part of the distance category.

Returning to the running example, the diagrams or maps at FIGS. 7-8 maybe used to illustrate the distance category. Regarding permeability inFIG. 8, permeability can be treated as distance (FIG. 8) or as a staticfeature (FIG. 9) depending on the embodiment. In the running example, at604, the dataset of allowed well connections may be modified accordingto the distance illustrated in the diagram of FIG. 7 to indicate allowedwell connections between the Inj-05 and Pro-04 pair, the Inj-05 andPro-05 pair, the Inj-05 and Pro-07 pair, the Inj-05 and Pro-08 pair, theInj-08 and Pro-07 pair, the Inj-08 and Pro-08 pair, the Inj-08 andPro-10 pair, and the Inj-08 and Pro-11 pair, as illustrated below:

TABLE 2 Dataset of allowed well connections modified by distancecategory Inj- Inj- per FIG. 7 . . . 05 . . . 08 Pro-01

 0

 0 Pro-02

 0

 0 Pro-03

 0

 0 Pro-04 1

 0 Pro-05 1

 0 Pro-06

 0

 0 Pro-07 1 1 Pro-08 1 1 Pro-09

 0

 0 Pro-10

 0 1 Pro-11

 0 1 Pro-12

 0

 0

Alternatively, in the running example, at 604, the dataset of allowedwell connections may be modified according to the distance illustratedin the diagram of FIG. 8 to indicate allowed well connections betweenthe Inj-05 and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-08 andPro-08 pair, and the Inj-08 and Pro-10 pair, as illustrated below:

TABLE 3 Dataset of allowed well connections modified by distancecategory Inj- Inj- per FIG. 8 . . . 05 . . . 08 Pro-01

 0

 0 Pro-02

 0

 0 Pro-03

 0

 0 Pro-04

 0

 0 Pro-05 1

 0 Pro-06

 0

 0 Pro-07 1

 0 Pro-08

 0 1 Pro-09

 0

 0 Pro-10

 0 1 Pro-11

 0

 0 Pro-12

 0

 0

As in the running example, within the distance category, the distanceassociated with FIG. 7 and the permeability associated with FIG. 8 ledto different well connections for the distance category. To resolve theconflict, modifying the dataset of allowed well connections accordingthe distance category may include using a priority within the distancecategory. In the running example, distance was selected overpermeability and therefore the distance was given the higher prioritywithin the distance category for determining the well connections. Ifthe running example included a second distance in addition to thedistance of FIG. 7 and the permeability of FIG. 8, then the highestpriority within the distance category may be the second distance, thendistance of FIG. 7, then the permeability of FIG. 8 for the allowedconnections.

At 606, the dataset of allowed well connections can be modified based ona static features category such as geological facies, etc. (e.g., samegeobody from 3D seismic data, permeability, etc.). The static featurescategory uses static data of the hydrocarbon reservoir, and the staticdata may include geological data, seismic data, three dimensional (3D)seismic data, faults, fractures, geobodies, geological boundaries, aprocessed version of any of these, or any combination thereof. If aparticular injection well and production well pair indicates a value of1, yes, or some other indication that the well connection does existbased on distance, but the static features suggest that a wellconnection does not exist for that pair, then the value for that wellconnection for that pair can be changed to 0, no, or some otherindication. By doing so, the static features have a higher priority thandistance, thereby selecting the highest priority. The priority betweencategories may be received from a user. Similarly, a priority within thestatic features category may be used and received from a user. A usermay designate the data that will be part of the static featurescategory.

Returning to the running example, the diagram or map at FIG. 9 may beused to illustrate the static features category. Assuming the previousvalues associated with FIG. 7, in the running example, at 606, thedataset of allowed well connections may be modified according to thestatic features (e.g., geobodies 1 and 2 from geological study and 3Dseismic data) illustrated in the diagram of FIG. 9 to indicate allowedwell connections between the Inj-05 and Pro-02 pair, the Inj-05 andPro-05 pair, the Inj-05 and Pro-07 pair, the Inj-08 and Pro-08 pair, theInj-08 and Pro-10 pair, the Inj-08 and Pro-11 pair, and the Inj-08 andPro-11 pair, as illustrated below:

TABLE 4 Dataset of allowed well connections modified by distancecategory per FIG. 9 . . . Inj-05 . . . Inj-08 Pro-01

 0

 0 Pro-02

 

 1

 0 Pro-03

 0

 0 Pro-04

 0

 0 Pro-05 1

 0 Pro-06

 0

 0 Pro-07 1

 0 Pro-08

 0 1 Pro-09

 0

 0 Pro-10

 0 1 Pro-11

 0 1 Pro-12

 0

 0

At 608, the database of allowed well connections can be modified basedon a dynamic features category such as 4D seismic data, tracer dataanalysis, pressure transient analysis, etc. (e.g., connected geobodyfrom 4D seismic data). The dynamic features category uses dynamic dataof the hydrocarbon reservoir, and the dynamic data may include wellproduction data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, capacitance resistancemodeling output data, polygon data, a processed version of any of these,or any combination thereof. If a particular injection well andproduction well pair indicates a value of 1, yes, or some otherindication that the well connection does exist based on the staticfeatures, but the dynamic features suggest that a well connection doesnot exist for that pair, then the value for that well connection forthat pair can be changed to 0, no, or some other indication. By doingso, the dynamic features have a higher priority than static features,thereby selecting the higher priority. The priority between categoriesmay be received from a user. Similarly, a priority within the dynamicfeatures category may be used and received from a user. A user maydesignate the data that will be part of the dynamic features category.

Returning to the running example, the diagrams and maps at FIG. 10 maybe used to illustrate the dynamic features category. Assuming theprevious values associated with FIG. 9, in the running example, at 608,the dataset of allowed well connections may be modified according to thedynamic features (e.g., connected volume1-4D data and connectedvolume2-tracer data) illustrated in the diagram of FIG. 10 to indicateallowed well connections between the Inj-05 and Pro-02 pair, the Inj-05and Pro-05 pair, the Inj-05 and Pro-07 pair, the Inj-05 and Pro-08 pair,and no well connections for Inj-08 but for Inj-11 instead, asillustrated below:

TABLE 5 Dataset of allowed well connections modified by dynamic featurescategory per FIG. 10 . . . Inj-05 . . . Inj-08 . . . Inj-11 Pro-01

 0

 0 Pro-02

 

 1

 0 Pro-03

 0

 0 Pro-04

 0

 0 Pro-05 1

 0 Pro-06

 0

 0 Pro-07 1

 0 Pro-08

 1

 0 Pro-09

 0

 0 Pro-10

 0

 0 1 Pro-11

 0

 0 1 Pro-12

 0

 0

Of note, the dynamic features category indicates that Pro-10 and Pro-11are connected to Inj-11 instead of Inj-08 based on the connected volume1 (4D seismic data) and connected volume2 (tracer data). FIG. 10 alsoillustrated a sealing fault referred to as the fault1. The fault1, whichmay be considered in the static features category, further confirms thatPro-10 and Pro-11 are connected to Inj-11 instead of Inj-08. Thus, acombination of static features and dynamic features can also be usedtogether as a category (e.g., during one iteration) in some embodiments,as illustrated in FIG. 10 with the fault1, the 4D seismic data, and thetracer data.

Furthermore, a sensitivity analysis can be run for each category and theallowed well connections per category can be based on the sensitivityanalysis per category. For example, the values in Table 2 for thedistance category may be based on a sensitivity analysis for thedistance category. In the sensitivity analysis, a first distance maylead to first values, a second distance may lead to second values, athird distance may lead to third values, and so on, and if the value of1 appears the most for a well pair then the value of 1 is selected forthat well pair but if the value of 0 appears the most for a well pairthen the value of 0 is selected for that well pair. A similar approachcan be pursued for the static features category, the distance featurescategory, or both. The sensitivity analysis for category may try toaccount for the variability (e.g., variability of a particular data itemin that category such as first 4D seismic data, second 4D seismic data,etc. in the dynamic features category or first geological boundary,second geological boundary, etc. in the static features category) andpotentially increase the accuracy of the allowed well connections forthe category, and in turn potentially increase the accuracy of the finalallowed well connections that are input to CRM.

At 610, the remaining well connections (and values thereof) after thedistance, static features, and dynamic features are the allowed wellconnections 308 that are input for CRM. As illustrated above, out of 24possible well pairs including Inj-05 and Inj-08, there are 4 allowedwell connections namely the Inj-05 and Pro-02 pair, the Inj-05 andPro-05 pair, the Inj-05 and Pro-07 pair, and the Inj-05 and Pro-08 andnone for Inj-08. The dataset of allowed well connections in Table 6 maybe used as input for CRM. Alternatively, the dataset of allowed wellconnections provided as input to CRM may only include the wellconnections with a value of 1.

TABLE 6 Dataset of allowed well connections . . . Inj-05 . . . Inj-08 .. . Inj-11 Pro-01 0 0 Pro-02 1 0 Pro-03 0 0 Pro-04 0 0 Pro-05 1 0 Pro-060 0 Pro-07 1 0 Pro-08 1 0 Pro-09 0 0 Pro-10 0 0 1 Pro-11 0 0 1 Pro-12 00

After running CRM, the output of CRM may be used to further modify thedataset of allowed well connections. For example, the output of CRM suchas the interwell connectivities (and Tau's) may be used to confirm wellconnections, add a new well connection (e.g., change 0 value to a 1value for a well pair in the dataset of allowed well connections),remove a well connection (e.g., change 1 value to a 0 value for a wellpair in the dataset of allowed well connections), etc. As an example, aninterwell connectivity value of 0 or below a threshold for a particularwell pair can lead to removing a well connection for that particularwell pair by selecting 0 for that well pair in the dataset of allowedwell connections. Indeed, CRM output available before or after runningCRM may be treated as part of the dynamic features category to remove oradd at least one allowed well connection to the dataset of allowed wellconnections. For instance, if dynamic data like tracer indicates noconnection exists between a particular well pair while pulse testindicates a connection does exist between that same well pair, and thenthe value can be set to 1 for the well pair if pulse test has a higherpriority but after running CRM, the CRM output indicates that the pulsetest was incorrect so the value is changed to 0 to indicate no wellconnection for that well pair. The dataset of allowed well connectionsrevised with the CRM output may be used as the initial dataset at 602.

Furthermore, those of ordinary skill in the art will appreciate thatvarious modification can be made to the disclosed embodiments. Forexample, in some embodiments, the initial dataset of allowed wellconnections can be based on the distance category, instead of assumingall well connections exist, and the initial dataset of allowed wellconnections may be modified based on the static features category, thedynamic features category, or both. Similarly, in some embodiments, theinitial dataset of allowed well connections can be based on the dynamicfeatures category, instead of assuming all well connections exist, andthe initial dataset of allowed well connections may be modified based onthe static features category, the distance category, or both. Similarly,the initial dataset of allowed well connections can be based onpreviously determined allowed well connections. In some embodiments, allor fewer than all categories may be used to determine the allowed wellconnections (e.g., the static features category only, the dynamicfeatures category only, or both). Furthermore, data may be received atdifferent points (e.g., not just at 602), and therefore the dataset ofallowed well connections may be modified a plurality of times before theallowed well connections are used as input to CRM.

FIG. 11 provides a method 1100, which is another embodiment of a methodfor determining allowed well connections. The method 1100 can beexecuted by the CRM application 214. The method 600 modifies a singledataset of allowed well connections whereas the method 1100 can generatea plurality of datasets (e.g., one dataset for a dynamic featurescategory, one dataset for a static features category, and one data setfor a distance category) and the plurality of datasets can be combinedinto a single dataset of allowed well connections based on priority.

At 1102, the method 1100 includes determining well connections based ona distance category (e.g., inverse distance). At 1104, the method 1100includes determining allowed well connections based on a static featurescategory such as geological facies, etc. (e.g., same geobody from 3Dseismic data). At 1106, the method 1100 includes determining allowedwell connections based on a dynamic features category such as 4D seismicdata, tracer data analysis, pressure transient analysis, etc. (e.g.,connected geobody from 4D seismic data). At 1108, the method 1100includes comparing the determined allowed well connections. At 1110, themethod 1100 includes combining the determined allowed well connectionsfrom the various datasets based on the comparisons and a priority (e.g.,combining based on the dynamic features category having the highestpriority of the three, then the static features category having the nexthigher priority, and then the distance category) into a combined datasetof allowed well connections. At 1112, the remaining allowed wellconnections after the distance, static features, and dynamic featuresare combined are the allowed well connections 310 that are input forCRM.

FIG. 12 provides an example consistent with the method 1100 andillustrates one dataset per category. The three datasets can be combinedinto one dataset of allowed well connections for input to CRM.Similarly, various datasets can be generated from FIGS. 7-10 andcombined according to method 1100.

The method 1100 can be accomplished by a computer executed method only(e.g., using thresholds, boundaries, location data, etc.), a user only(e.g., for more flexibility), or both a computer executed method and auser (e.g., for more flexibility) as described hereinabove for method600. Furthermore, in some embodiments, the order of distance, thenstatic features, and then dynamic features of the method 600 can also bechanged in the method 1100 such that the dynamic features are higherpriority and used first at 1102, then the static features, then thedistance. Moreover, there can be a priority between categories (e.g.,dynamic features, then static features, then distance) and/or a prioritywithin a category (e.g., geobodies have a higher priority thanpermeability within the static feature category or vice versa).Furthermore, as discussed in connection with FIG. 6, the prioritybetween categories may be received from a user. Similarly, a prioritywithin a category may be used and received from a user. Sensitivityanalysis per category may also be performed. Moreover, a user maydesignate the data that will be part of the categories. After runningCRM, the output of CRM may also be used to further modify the allowedwell connections.

Returning to FIG. 3, at 312, CRM can be run using the allowed wellconnections as input to CRM from 310 and the other input to CRM from306. For example, the CRM application 214 can run CRM to quantify theinterwell connectivities using techniques known in the art, such asthose in the documents incorporated herein. However, consistent withthis disclosure, CRM can be modified to quantify the interwellconnectivities in a manner that is consistent with the available data(e.g., from FIG. 5), consistent with the allowed well connectionsdetermined in FIGS. 6, 11 that are input, etc. In other words, CRM canbe provided with constraints to enforce consistency with the availabledata (e.g., the 4D seismic data, the pressure and saturation from the 4Dseismic data, the pressure and saturation maps, etc.) and the allowedwell connections. Table 7 (below) illustrates an example of potentialinterwell connectivities that can be generated by CRM:

TABLE 7 Interwell Connectivities (F_(ij)) generated with allowed wellconnections as an input to CRM . . . Inj-05 . . . Inj-08 . . . Inj-11Pro-01 0 0 Pro-02 .1 0 Pro-03 0 0 Pro-04 0 0 Pro-05 .3 0 Pro-06 0 0Pro-07 .4 0 Pro-08 .2 0 Pro-09 0 0 Pro-10 0 0 .3 Pro-11 0 0 .2

At 314, practically any data available thus far can be integrated withat least one application or component by a computer executed methodonly, a user only, or both a computer executed method and a user. Forexample, the data available at this point can be the most current data(e.g., the most current interwell connectivities determined by CRM, themost current allowed well connections, the most current 4D seismic data,the most current pressure and saturation from the 4D seismic data,etc.). An application that uses or receives as input that type of datacan be updated to integrate the data, for example, the application canbe the petrotechnical mapping application 220, a simulation application,etc. such as illustrated in FIG. 2A, 2C.

Furthermore, at 314, additional interpretation can occur. For example,the additional interpretation can be a check for consistency, can findany inconsistencies, etc., which may also lead to integration of datainto at least one application. The interpretation can happen before orafter integration of data into at least one application. Control canpass to 304 to restart the loop.

At 316, reservoir decisions can be addressed. For example, arecommendation can automatically be generated based on the availabledata. Alternatively, a user can make at least one reservoir decisionbased on the available data. The reservoir decisions can relate tooptimization, history matching, conformance control, changing aparameter of the flood operation, etc., for example, to improve theflood operation. Control can pass to 302 to restart the loop to receivemore field data 222.

At the zonal level, a similar approach can be used. In some embodiments,each zone may be treated as an injection well, and allowed wellconnections for each injection well/zone and production well pair can bedetermined as described hereinabove in FIGS. 6, 11. For example, at 302,the received field data can include zonal splits data from the zonalapplication 218 that indicates the percentage of water, oil, gas, and/orinjection (e.g., water injection) per zone. At 304, the received fielddata at the zonal level can be further processed as illustrated in FIG.3, for example, to determine continuity of zones from the well log data504. At 308, 310, the allowed well connections at the zonal level can bedetermined as illustrated in FIGS. 6, 11 and an example is illustratedbelow at Table 8.

In Table 8, 0's and 1's are illustrated for each zone and productionwell pair. Table 8 assumes the existence of 3 zones in the reservoir, aplurality of injection wells, and a plurality of production wells. Table8 also assumes that only the injection well Inj-01 has perforations andfluidic contact with the three zones. In this example, there are 10allowed well connections out of a possible 15 because the analysis is atthe zonal level. At the well level, there would have been 9 possibleallowed well connections, but Table 8 illustrates 10 allowed wellconnections out of a possible 15 at the zonal level:

TABLE 8 Dataset of allowed well connections at the Inj-01 Inj-01 Inj-01zonal level zone-01 zone-02 zone-03 Inj-02 Inj-03 . . . Pro-01 0 0 1 1 1Pro-02 1 1 0 1 1 Pro-03 1 0 0 1 1 . . .

Furthermore, as discussed in connection with FIGS. 6, 11, a prioritybetween categories may be used and received from a user. Similarly, apriority within a category may be used and received from a user.Sensitivity analysis per category may also be performed. Moreover, auser may designate the data that will be part of the categories.

At 312, CRM can be run using the allowed well connections at the zonallevel as input to CRM and the other input to CRM. After running CRM, theoutput of CRM may also be used to further modify the allowed wellconnections at the zonal level, as discussed in connection with FIGS. 6,11. At 314, the available data at the zonal level at this point can beintegrated with at least one application. At 316, reservoir decisionscan be addressed at the zonal level, such as optimization at the zonallevel, conformance control at the zonal level, history matching at thezonal level, changing a parameter at the zonal level, etc.

Those of ordinary skill in the art will appreciate that variousembodiments and modifications are envisioned. For example, the input toCRM may be the entire dataset of allowed well connections with all ofthe 0's and 1's (such as from FIG. 6) or the entire combined dataset ofallowed well connections with all of the 0's and 1's (such as from FIG.11) at the well level. For example, the input to CRM may be the entiredataset of allowed well connections with all of the 0's and 1's (such asfrom FIG. 6) or the entire combined dataset of allowed well connectionswith all of the 0's and 1's (such as from FIG. 11) at the zonal level.For example, the input to CRM may be the dataset of allowed wellconnections with only the 1's (such as from FIG. 6) or the combineddataset of allowed well connections with only the 1's (such as from FIG.11) at the well level. For example, the input to CRM may be the datasetof allowed well connections with only the 1's (such as from FIG. 6) orthe combined dataset of allowed well connections with only the 1's (suchas from FIG. 11) at the zonal level. Other modifications will becomeevident to those of ordinary skill in the art.

Those of ordinary skill in the art will appreciate that the accuracy ofCRM can potentially be increased by the allowed well connections thatare determined as the well level and/or at the zonal level. For example,the allowed well connection are based on dynamic features (e.g., 4Dseismic data), static features, and/or distance for the reservoir,therefore potentially leading to improvements in the analysis of floodoperations. Moreover, hidden faults and fractures are more likely to beidentified and used in the analysis, multiple field data typessuggesting the same are more likely to be identified and used in theanalysis (e.g., tracer data and pulse test data supporting the existenceof a well connection), conflicting data is more likely to be resolved(e.g., through priorities), etc.

Furthermore, in some embodiments, the analysis of a flood operation canbe carried out in a faster or more efficient manner as only allowed wellconnections with 1's are input to CRM and/or processed further by CRM.In some examples, the analysis of the flood operation may speed up about200 times or running CRM may decrease from hours, such as about 400hours, to minutes, such as about 20 minutes.

Furthermore, those of ordinary skill in the art may appreciate that moreaccurate CRM output may be used to make at least one decision that islikely to improve the flood operation. The decisions may be related tooptimization, history matching, conformance control, changing aparameter of the flood operation, or any combination thereof.

Heavy Oil Flood Operation

The hydrocarbon in some hydrocarbon reservoirs is referred to as heavyoil. A flood operation, such as waterflood operation, may be performedon a hydrocarbon reservoir having heavy oil. Disclosed herein areembodiments for using CRM to analyze a flood operation on a hydrocarbonreservoir having heavy oil. As will be discussed herein, various timewindows may be generated and CRM may be run for each time window so asto account for mobility, time, or other items characteristics that arespecific to hydrocarbon reservoirs having heavy oil. By doing so, CRMoutput may be more accurate and the analysis of the flood operation maybe more accurate.

An embodiment of a computer implemented method of analyzing a floodoperation on a hydrocarbon reservoir having heavy oil, referred to as1400 in FIG. 14. The method 1400 can be executed by a computing system,such as the computing system 200 of FIG. 2A or the CRM application 214thereof. After executing the method 2000 of FIG. 20, the resultingoutput may be used as input to update or integrate with the polygonapplication 216, the zonal application 218, the petrotechnical mappingapplication 220, the one or more other application 221, the floodinganalysis application 212, and/or other item (as in FIGS. 2A, 2C). Forease of understanding, a running example will be used and the runningexample assumes one injection well and three production wells.

At 1402, the method 1400 receives injection rate data and productionrate data for production wells communicating with the first injectionwell for a period of time. For example, the method receives well names,well locations, injection rates, and production rates. At 1404, themethod 1400 establishes a plurality of time windows based onbreakthrough time of each production well that has broken through untilthe current date, wherein the production rate data is indicative ofbreakthrough. For example, a time window may be established for eachproducer that has broken through, and optionally, an extra time window.For example, 4 producers that have broken through may lead toestablishing 4 or 5 time windows. In some embodiments, 10% watercut maybe used as the breakthrough time indicator. In the running example,dates D2 and D3 are at 10% watercut. As such, a first time window fromtime 0-time 15 is established for the first producer to breakthrough andreach watercut of 10%, a second time window from time 15-time 60 isestablished for the second producer to breakthrough and reach watercutof 10%, and a third time window from time 60-time 130 is established forall producers to breakthrough and reach watercut of 10%, as illustratedbelow:

data Time, month CRM-fij for I1-P1 CRM-fij for Inj1-P2 CRM Runs 0 1 1 22 first window 3 3 at date D1 4 4 5 6 6 8 7 10 0.743155395 0.256844605Run1 - No Breakthrough 8 15 D1 9 25 0.784838501 0.215161499 SlidingWindow Run - Overlap Time Window 10 30 second window 11 38 0.7923839440.207616056 Run2 - Early stage post breakthrough of first well-P1 atdate D2 12 50 13 60 D2 - 10% watercut first producer 14 75 15 80 16 880.922930965 0.077069035 Sliding Window Run - Overlap Time Window 17 9518 100 third window 19 110 0.890533376 0.109466624 Run3 - Early stageafter BT of another well-P2 (repeat at date D3 20 120 when another wellBT) 21 130 D3 - 10% watercut second producer 22 132 23 133 0.9094245540.090575446 Sliding Window Run - Overlap Time Window 24 142 25 149 26150 27 153 28 161 29 166 30 172 31 181 0.899863617 0.100136383 Run4 -After Breakthrough of All Wells until the 32 182 last data point 33 191D4: current date 34 201 35 211 36 221 37 231 38 241 39 251 40 261

At 1406, CRM may be run for each established time window to generateinterwell connectivities. In the running example, for the first timewindow ending at date D1, the CRM output may include the followinginterwell connectivities fijs:

CRM-fij for I1-P1 CRM-fij for Inj1-P2 First time window 0.7431553950.256844605

In the running example, for the second time window ending between D1 andD2, the CRM output may include the following interwell connectivitiesfijs:

CRM-fij for I1-P1 CRM-fij for Inj1-P2 Second time 0.7848385010.215161499 window 0.792383944 0.207616056

In the running example, for the third time window ending between D3 andD2, the CRM output may include the following interwell connectivitiesfijs:

CRM-fij for I1-P1 CRM-fij for Inj1-P2 Third time window 0.9229309650.077069035 0.890533376 0.109466624

Optionally, at 1408-1410, at least one sliding time window may beestablished and CRM may be run for each sliding time window. The slidingtime window may be established to improve the distribution of theinterwell connectivity variation between CRM runs 1, 2, 3, and 4. Aparticular sliding time window may be established between two timewindows established at 1404. In the running example, a sliding timewindow may be established from time 25-time 88, another sliding timewindow may be established from time 88-time 133, and another slidingtime window may be established from time 133-time 191. The followingtables indicate the interwell connectivities for the sliding timewindows.

CRM-fij for I1-P1 CRM-fij for Inj1-P2 first sliding time 0.7848385010.215161499 window 0.792383944 0.207616056 0.922930965 0.077069035

CRM-fij for I1-P1 CRM-fij for Inj1-P2 Second sliding time 0.9229309650.077069035 window 0.890533376 0.109466624 0.909424554 0.090575446

CRM-fij for I1-P1 CRM-fij for Inj1-P2 third sliding time 0.9094245540.090575446 window 0.899863617 0.100136383

At 1412, the method 1400 may solve a continuous function for thegenerated interwell connectivity of each well pair over time using thediscrete interwell connectivities obtained in 1406-1410. The continuousfunction may be a bell shaped, log-normal, betta, logistic curve, orother. In the running example, the interwell connectivity variation plotat FIG. 15 may be generated by plotting the interwell connectivitiesgenerated by CRM and then at least one function may be used to fit theinterwell connectivity points (e.g., logistic curve) on a curve. Thecurve and the interwell connectivity points may be displayed to a userin some embodiments, but not in other embodiments.

In the running example, solving the continuous function may includesolving the parameters of the function by matching function estimatedinterwell connectivities to the CRM generated interwell connectivities.Matching the interwell connectivities includes minimizing the errorbetween the function estimated interwell connectivities and the CRMgenerated interwell connectivities. The parameters and the functionestimated fij match values are illustrated below:

Initial fij Growth Decline Rate Peak Time, Value Rate (G) (D)Integration, N Years 0.7 0.05 0.005 90 65 0.3 0.05 0.005 90 65 FunctionFunction estimated fij estimated fij Time, Match for Match for monthI1-P1 I1-P2 0 0.729400211 0.270599789 1 0.730789629 0.269210371 20.732238642 0.267761358 3 0.733749198 0.266250802 4 0.7353232440.264676756 6 0.738669556 0.261330444 8 0.742292858 0.257707142 100.746207837 0.253792163 15 0.757357586 0.242642414 25 0.7859038430.214096157 30 0.803196639 0.196803361 38 0.833762714 0.166237286 500.878277722 0.121722278 60 0.901382431 0.098617569 75 0.9044176670.095582333 80 0.904258082 0.095741918 88 0.903870664 0.096129336 950.903399187 0.096600813 100 0.902987362 0.097012638 110 0.9019783630.098021637 120 0.900726493 0.099273507 130 0.899237858 0.100762142 1320.898912333 0.101087667 133 0.898746146 0.101253854 142 0.8971491590.102850841 149 0.895783723 0.104216277 150 0.895580055 0.104419945 1530.894956349 0.105043651 161 0.893201722 0.106798278 166 0.8920393210.107960679 172 0.890579879 0.109420121 181 0.888263626 0.111736374 1820.887997153 0.112002847 191 0.885520243 0.114479757 201 0.8826097050.117390295 211 0.879544368 0.120455632

At 1414, the production rate data is used to solve an oil-cut model foreach production well that has broken through to estimate oil-cut valuefor each production well in the future. In the running example, thereceived production rate data for each producer may be used to solve theparameter of the oil cut model (e.g., power law model). Alpha and beta(below) are the parameters of the oil cut model and a plot of oil cut isillustrated in FIG. 16:

Oil-cut Model breakthrough time 30 100 alpha 0.1 0.2 beta 0.5 0.7 OilCut- Time, month P1 Oil Cut-P2 0 1 1 1 2 1 1 3 1 1 4 1 1 6 1 1 8 1 1 101 1 15 1 1 25 1 1 30 1 1 38 0.7795188 1 50 0.690983 1 60 0.6461106 1 750.5985084 1 80 0.5857864 1 88 0.5676731 1 95 0.5536406 1 100 0.5444666 1110 0.527864 0.499407 120 0.513167 0.380465 130 0.5 0.316176 1320.4975247 0.306491 133 0.4963052 0.301931 142 0.4858377 0.267581 1490.4782695 0.24697 150 0.4772256 0.24435 153 0.4741463 0.236897 1610.4662978 0.21957 166 0.4616399 0.210266 172 0.4562798 0.200331 1810.4486678 0.187448 182 0.4478515 0.186144 191 0.4407504 0.175352 2010.4333376 0.165047 211 0.4263733 0.15614

At 1416, the value of injected fluid as a function of time in future maybe determined for each injector using the continuous function estimatedinterwell connectivity 1412 and the estimated oil-cut value of 1414. Inthe running example, VOIF(t) is calculated for the current timestep D4and the timesteps in the forecast time window (1 or 2 or 3 years afterthe current date D4) and get an average VOIF. The following equation maybe calculated to VOIF(t) for each injector:

${V\; O\; I\;{F(t)}} = {\sum\limits_{j}^{N}{{f_{ij}(t)}{f_{oj}(t)}}}$

In the equation, f_(ij)(t) is oil cut as a function of time andf_(ij)(t) is interwell connectivity as a function of time. The (t) inthe equation, such as in VOIF(t), is a function of time. In someembodiments, the f_(ij)(t) may be at a steady state as discussed herein.Alternatively, if transient is desired, then fij(t) may be replaced withf*ij(t) in the equation. In the running example, 0.39 is the averageforecast VOIF after current time D4 for I1, as illustrated below and atFIG. 17:

Average forecast VOIF post D4 for I1 0.39 Time, VOIF I1 as a function ofmonth time 0 1 1 2 1 3 1 4 1 6 1 8 1 10 1 15 1 25 1 30 1 38 0.816 500.729 60 0.681 75 0.637 80 0.625 88 0.609 95 0.597 100 0.589 110 0.525120 0.500 130 0.481 132 0.478 133 0.477 142 0.463 149 0.454 150 0.453153 0.449 161 0.440 166 0.435 172 0.428 181 0.419 182 0.419 191 0.410D4: current date 201 0.402 211 0.394 221 0.386 231 0.379 241 0.372

At 1418, a plurality of injectors may be ranked based on averageforecast VOIF (accounts for time) values for each injector for theforecasted time window, such as from current time to a defined period oftimein. In the running example, there is only 1 injector, and it has asan average forecast VOIF of 0.39. However, this injector could be rankedagainst another injector.

At 1420, the generated average forecast value of injected fluid for thefirst injection well is compared with a threshold to determine if thefirst injection well is a candidate for stimulation. For example, a lowaverage forecast VOIF may indicate that the corresponding injector is acandidate for stimulation (e.g., stimulation with an acid agent).

Those of ordinary skill in the art will appreciate that variousmodifications may be made to the embodiments disclosed herein. Forexample, all producers had broken through in the running example, butsuch need not be the case. For example, if only a portion of theproducers had broken through, then oil cut can equal 1 for injectorswith corresponding producers that have not broken through. Also, thenumber of time windows may be based on producers broken through, so thefewer producers broken through then the fewer time windows may beestablished.

Those of ordinary skill may appreciate the illustrate embodiments maylead to more accurate analysis of flood operations of reservoirs havingheavy oil Furthermore, the method 1400 may also be able to recommend aninjection rate for each injector based on the ranking per averageforecast VOIF and operation limits. The method 1400 may output (e.g.,display, print, etc.) the injection rate recommendation and a user maythen physically make the appropriate adjustments to achieve therecommend injection rate for a particular injector. Adjusting theinjection rate based on the ranking per average forecast VOIF from andoperational limits of injectors and producers may improve hydrocarbonrecovery.

Conformance Control

A flood operation sometimes leads to non-uniform volumetric sweep or adeviation from uniform volumetric sweep in a reservoir because of thereservoir heterogeneity, unfavorable mobility ratio, gravitysegregation, gas override, well placement, perforations, completions,casing, etc. Such a scenario is known as a conformance problem andhydrocarbon production may increase by conformance control. Based oninput data, different tools may be used as shown in the table below:

Well Profiles (static Modified and Data/ EV CRM Numerical Hall dynamicEvaluation Estimates Koval CRM Zonal Simulation Tracer Plot logs) Modelx x x x x x calibration Logs, x x x x Core data & tests Injection/ x x xx x x x Production data Water x x chemistry Oil/gas x x chemistryInjection x x x profiles Production x x x profiles Reservoir x x x xpressures Flowing x x x x BHPs Tracer x x x x data Saturation x x logsSeismic x x x data Flow- x x x x x x Storage Capacity

To screen for conformance issues where an injecting entity (an injectionentity can be a field, a reservoir field, reservoir, well or zone)causes early and excessive displacing fluid production at a producingentity (a producing entity can be a field, a reservoir, a well, or azone) and reduces hydrocarbon recovery, analytical tools such as CRM,modified Koval method (MKM), modified Hall Plot, tracer and flow-storagecapacity plotsand, etc. may be used. To identify and rank conformancecandidates at the level of any injecting and producing entity (e.g., afield, a reservoir, a well, or a zone), a conformance problem index(CPI) is introduced herein.

Well Profiles (static Modified and Numerical Hall dynamic Method KovalCRM Simulation Tracer Plot logs) Applied to Field/Reservoir Level X X XScreening Applied to Injection Entity X X X X Screening Applied toProduction Entity X X X Screening Applied to Injection Entity - X X X XZonal Level Applied to Production Entity - X X X X Zonal Level AppliedInjection-Production X X X Entity Pair Level Screening

An embodiment of a computer implemented method of identifying at leastone conformance candidate (e.g., using the CPI and components thereof)is provided herein, referred to the method 1800 (numbers 1802-1820) inFIG. 18. The method 1800 may correspond to the diagnostic & screeningportion of the conformance control workflow of FIG. 13. The method 1800can be executed by a computing system, such as the computing system 200of FIG. 2A. After executing the method 2000 of FIG. 20, the resultingoutput may be used as input to update or integrate with the CRMapplication 214, polygon application 216, the zonal application 218, thepetrotechnical mapping application 220, the one or more otherapplication 221, the flooding analysis application 212, and/or otheritem (e.g., as illustrated in FIGS. 2A, 2C).

Defining Conformance Problem Index (CPI): As disclosed herein, a CPI isevaluated at the level of injecting and producing entities by definingand evaluating its three main components, which are Injection EntityIndex (IEI), Production Entity Index (PEI) and Operational Index (Op, asillustrated below:CPI=IEI*PEI*OI

The CPI value ranges between 0 and 1, the higher the CPI value thehigher the degree of conformance issue in the entity (e.g.,producing/injecting entities). Two running examples will be presented toillustrate use of the CPI at a field/reservoir level and at a well/zonelevel.

Defining Injection Entity Index: An IEI is defined by combining (i)injection efficiency and (ii)) Value of Injected Fluid (VOIF) obtainedbetween at least one injecting entity and one producing entity pair(e.g., an injecting-producing entity pair could be atreservoir-reservoir level, injector-producer level, or injector-producerat zonal level) and (iii) pore volume injected (PVI) for each injectingentity. This calculation can be obtained based on analytical methodssuch as capacitance-resistance model (CRM), tracer analysis, or anycombination thereof. In some embodiments, fij, may also be used.Nonetheless, an IEI ranges between 0 and 1, and may be solved accordingto the equation below:IEI=(1−f _(ij))*VOIFij/PVIi

Defining Producing Entity Index (PEI): A PEI is defined by evaluating anestimate (or a proxy of fraction) of remaining moveable oil in place atone pore volume injected for at least one producing entity. Thiscalculation can be obtained based on analytical methods such as modifiedKoval method (MKM), tracer analysis, or any combination thereof. Arecovery factor estimate, RF@1PVI is used in calculating PEI. A PEIranges between 0 and 1, and may be solved according to the equationbelow:PEI=1−RF@1PVI

Defining the Operational Index (OI): An OI is a value of either one orzero which represents operation status (e.g., the availability of agiven producing or injecting entity) for any conformance treatment.

Application when injection and production entities arefield/reservoir—the field/reservoir level injection efficiency for anexample of four field/reservoir is shown below:

CRM or Tracer Output 1 Injection Efficiency between Injection EntitiesInjection and Production (Field or Reservoir) Entities IE 01 IE 02 IE 03IE 04 Production PE 01 0.75 Entities PE 02 0.40 (Field or PE 03 0.92Reservoir) PE 04 0.60 Sum 0.75 0.4 0.92 0.6

Application when injection and production entities arefield/reservoir—the field/reservoir level value of injected fluid (VOIF)for an example of four field/reservoir is shown below:

CRM Output 2 Injection Entities (Field or Reservoir) Value of InjectedFluid IE 01 IE 02 IE 03 IE 04 Production PE 01 0.82 Entities PE 02 0.43(Field or PE 03 0.05 Reservoir) PE 04 0.30 Sum 0.82 0.43 0.05 0.3

Application when injection and production entities arefield/reservoir—the field/reservoir level pore volumes injected (PVI)for an example of four field/reservoir is shown below:

Injection Entities (Field or Reservoir) IE 01 IE 02 IE 03 IE 04 PoreVolume Injected in 1.2 0.7 2.1 1.8 Analysis Period

Application when injection and production entities arefield/reservoir—the field/reservoir level Injection Entity Index (IEI)for an example of four field/reservoir is calculated from IEI equationabove and is shown below, in this example IEI is highest forfield/reservoir 3:

Injection Entities (Field or Reservoir) Injection Entity Index (IEI) IE01 IE 02 IE 03 IE 04 Production PE 01 0.11 Entities PE 02 0.33 (Field orPE 03 0.42 Reservoir) PE 04 0.23

Application when injection and production entities arefield/reservoir—the field/reservoir level Production Entity Index (PEI)for an example of four field/reservoir is calculated from the estimatesof recovery factors at one pore volume injected are shown below in thisexample the PEI is highest for field/reservoir 3:

Modified Koval Method Output 1 Recovery Factor based on Productionmovable oil at 1 Pore Volume Entity Index Injected (PEI) Production PE01 0.40 0.60 Entities PE 02 0.32 0.68 (Well or PE 03 0.15 0.85 Zone) PE04 0.20 0.80

Application when injection and production entities arefield/reservoir—the field/reservoir level Operational Index (OI) for anexample of four field/reservoir is shown below, in this example the OIis one for all field/reservoir indicating availability of all candidatesfor conformance treatment:

Operational Condition/Stability Injection Entities (Field or Reservoir)Operational Index (OI) IE 01 IE 02 IE 03 IE 04 Production PE 01 1Entities PE 02 1 (Field or PE 03 1 Reservoir) PE 04 1

Application when injection and production entities arefield/reservoir—the field/reservoir level Conformance Problem Index(CPI) for an example of four field/reservoir is calculated from CPIequation above and is shown below, in this example the CPI is highestfor field/reservoir 3 indicating the field/reservoir with highest degreeof conformance issue:

Injection Entities (field) Conformance Problem Index IE 01 IE 02 IE 03IE 04 Production PE 01 0.07 Entities PE 02 0.22 (Field or PE 03 0.35Reservoir) PE 04 0.19

Application when injection and production entities are well/zone—thewell/zone level injection efficiency for an example of four well/zone isshown below:

CRM Output 1 Injection Efficiency between Injection Entities Injectionand Production (Well or Zone) Entities IE 01 IE 02 IE 03 IE 04Production PE 01 0.96 0.47 0.10 0.18 Entities PE 02 0.01 0.02 0.02 0.15(Well or PE 03 0.00 0.19 0.02 0.00 Zone) PE 04 0.03 0.32 0.86 0.67 Sum 11 1 1

Application when injection and production entities are well/zone—thewell/zone level value of injected fluid (VOIF) for an example of fourwell/zone is shown below:

CRM Output 2 Injection Entities (Well or Zone) Value of Injected FluidIE 01 IE 02 IE 03 IE 04 Production PE 01 0.30 0.15 0.03 0.06 Entities PE02 0.00 0.01 0.01 0.06 (Well or PE 03 0.00 0.03 0.00 0.00 Zone) PE 040.01 0.06 0.15 0.12 Sum 0.31 0.24 0.19 0.23

Application when injection and production entities are well/zone—thewell/zone level pore volumes injected (PVI) for an example of fourwell/zone is shown below:

Injection Entities (Well or Zone) IE 01 IE 02 IE 03 IE 04 Pore VolumeInjected in 1.00 2.00 1.20 2.10 Analysis Period

Application when injection and production entities are well/zone—thewell/zone level Injection Entity Index (JET) for an example of fourwell/zone is calculated from JET equation above and is shown below, inthis example JET is highest for well/zone IE01:

Injection Entities (Well or Zone) Injection Entity Index (IEI) IE 01 IE02 IE 03 IE 04 Production PE 01 0.67 0.20 0.08 0.08 Entities PE 02 0.010.01 0.02 0.07 (Well or PE 03 0.00 0.09 0.02 0.00 Zone) PE 04 0.03 0.150.61 0.28

Application when injection and production entities are well/zone—thewell/zone level Production Entity Index (PEI) for an example of fourwell/zone is calculated from the estimates of recovery factors at onepore volume injected are shown below in this example the PEI is highestfor well/zone PEW:

Modified Koval Method Output 1 Recovery Factor based on movable oil at 1Pore Production Entity Index Volume Injected (PEI) Production PE 01 0.200.80 Entities PE 02 0.40 0.60 (Well or PE 03 0.56 0.44 Zone) PE 04 0.370.63

Application when injection and production entities are well/zone—thewell/zone level Operational Index (OI) for an example of four well/zoneis shown below, in this example the OT is one for all well/zoneindicating availability of all candidates for conformance treatment:

Operational Condition/Stability Injection Entities (Well or Zone)Operational Index (OI) IE 01 IE 02 IE 03 IE 04 Production PE 01 1 1 1 1Entities PE 02 1 1 1 1 (Well or PE 03 1 1 1 1 Zone) PE 04 1 1 1 1

Application when injection and production entities are well/zone—thewell/zone level Conformance Problem Index (CPI) for an example of fourwell/zone is calculated from CPI equation above and is shown below, inthis example the CPI is highest for well/zone 3 indicating the well/zonewith highest degree of conformance issue:

Conformance Injection Entities (Well) Problem Index IE 01 IE 02 IE 03 IE04 Production PE 01 0.53 0.16 0.06 0.06 Entities PE 02 0.01 0.00 0.010.04 (Well or PE 03 0.00 0.04 0.01 0.00 Zone) PE 04 0.02 0.09 0.38 0.18

After an entity (e.g., a well) is identified as a conformance candidate,a conformance control treatment may be determined. FIGS. 19A-19Billustrates one embodiment of a computer implemented method ofdetermining a conformance control treatment for a well of a hydrocarbonreservoir, where the well is in fluidic communication with a pluralityof zones of the hydrocarbon reservoir, referred to herein as method1900. The method 1900 may correspond to modeling optimum slugdesign/placement portion of the conformance control workflow of FIG. 13.The method 1900 can be executed by a computing system, such as thecomputing system 200 of FIG. 2A. After executing the method 1900 of FIG.20, the resulting output may be used as input to update or integratewith the CRM application 214, polygon application 216, the zonalapplication 218, the petrotechnical mapping application 220, the one ormore other application 221, the flooding analysis application 212,and/or other item (e.g., as illustrated in FIG.). The method 1900 may beused to determine which zone or zones should be treated with aconformance agent, determine slug size, determine concentration of theconformance agent, and/or determine when to perform the conformancecontrol treatment. The well may be identified as discussed herein inconnection with FIG. 18 or using some other process. A running examplewill be discussed that assumes a hydrocarbon reservoir with at least oneinjection well and five zones. The running example also assumes aninjection rate of 2500 bbls/day, a pore volume of 100,000 bbls, and athreshold for residence time distribution of 10 days.

At 1902, data for each zone of the plurality of zones of the hydrocarbonreservoir may be received. The data includes depth, porosity,permeability, and pore volume for each of the zones. In the runningexample, the following data is received for the zones.

Input Data Reservoir Depth Porosity Permeability Pore volume Zones (ft)Fraction (md) Bbls 485 0 0 1 500 0.3 100 26,316 0.01 2 503 0.3 10005,263 0.1 3 535 0.3 10 56,140 0.001 4 540 0.3 500 8,772 0.05 5 542 0.35000 3,509 0.5

At 1904, a residence time distribution (or proxy thereof) may bedetermined for each zone. The residence time distribution may bedetermined using CRM zonal, F−C Plot, tracers, etc. The F−C Plot or flowcapacity-Storage capacity is a static plot where both flow and storagecapacity information are obtained from static log data, permeability,porosity, and thickness. In some embodiments, the F+C plot may be used.The F+C plot is a semi-dynamic plot—constructed similar to an F−C plot,but the flow information is from profile data and the storage capacityinformation is from porosity logs. In some embodiments, the F-Phi plotmay be used. The F-Phi plot is a dynamic plot and both flow and storageinformation are from dynamic production data. Indeed, in someembodiments, determining the residence time distribution for each zoneof the plurality of zones includes using tracer data, CRM zonal, a F−Cplot, F+C plot, F-Phi plot, log data, ILT data, CRM generated PLT, slopedata, or any combination thereof. In the running example, the followingresidence time distributions are determined for the zones.

Original residence time distribution before treatment 1 Residence timeResidence time distribution using CRM distribution using CRM ReservoirZonal, F-C, Tracers, etc. Zonal, F-C, Tracers, etc. Zones tD,Dimensionless t, days 5 0.06 2 2 0.30 12 4 0.61 24 1 3.04 122 3 30.391215

At 1906, at least one zone of the plurality of zones may be identifiedto be treated with a conformance agent based on a comparison of thedetermined residence time distribution of each zone to a residence timedistribution threshold. In the running example, the threshold forresidence time distribution is 10 days and any zone with residence timedistribution of less than or equal to the threshold may be identifiedfor conformance control, which would likely shut off that zone. Thethreshold may be received from a user or automatically generated. In therunning example, zone 2 has a residence time distribution of 2 days,which is lower than the threshold of 10 days, thus, zone 2 is identifiedfor treatment with a conformance agent (treatment 1). A residence timedistribution of 2 days indicates that the displacing fluid, such aswater or brine from a waterflood operation, breaks through in about 2days which is before the threshold of 10 days.

At 1908, breakthrough time of slowest identified zone may be determined.The breakthrough time may be determined from the residence timedistributions determined at 1904 for the corresponding identified zones.In the running example, only zone2 was identified and the residence timedistribution is 2 days for zone 2 so the breakthrough time is determinedto be 2 days. If another zone had been identified, then the slowestresidence time distribution from the identified may be selected, whichwill be described further in connection with treatment2.

At 1910, a first conformance control treatment may be recommended at thedetermined breakthrough time of the slowest identified zone. In therunning example, a first conformance control treatment (treatment1) maybe recommended at 2 days for zone 2, as illustrated below.

Treatment 1 Time of conformance treatment is equal to breakthrough 2days of the slowest of the zones to be shut off (days) Slug Size (bbls)is half of the injected volume before 3039 bbls breakthrough of thefastest zones Resistance factor to be used to reduce its velocity to 171/10th of its original velocity Concentration of the conformance agentto be used based 20776 ppm on the rheology Sweep Efficiency beforetreatment @ 1 PV injected 0.25 Sweep efficiency after treatment 1 @ 1 PVinjected 0.5

At 1912, a recommend slug size for the first conformance controltreatment may be determined. The slug size is determined by calculatinginjected volume for a period of time which is not more than 50% of abreakthrough time for the fastest identified zone. In the runningexample, a first conformance treatment slug size (treatment 1) isrecommended to be 3039 Bbls.

At 1914, a recommended concentration of the conformance agent fortreatment 1 may be determined by calculating a resistance factor of theconformance agent sufficient to reduce the original velocity of thefastest identified zone by a factor of about 10. The conformance agentrheology may be used to determine the concentration after the resistancefactor is calculated. In the running example, the resistance factor forthe first conformance agent (treatment 1) is recommended to be 17. Fromthe rheology for the conformance agent, the recommended conformanceagent concentration is determined to be 20776 ppm so that a resistancefactor of 17 is achieved.

At 1916, data for each zone of the plurality of zones of the hydrocarbonreservoir may be updated after treatment 1. At least one of porosity,permeability, pore volume, or any combination thereof for each of theplurality of zones of the hydrocarbon reservoir may be updated aftertreatment 1. In the running example, the following data is updated forthe zones.

Input Data Reservoir Depth Porosity Permeability Pore volume Zones (ft)Fraction (md) Bbls 485 0 0 1 500 0.3 86 26,316 0.08 2 503 0.3 385 5,2630.35 3 535 0.3 10 56,140 0.01 4 540 0.3 278 8,772 0.25 5 542 0.3 5563,509 0.50

At 1918, a residence time distribution (or proxy thereof) may bedetermined for each zone after the first treatment. The new residencetime distribution may be determined using updated CRM zonal, F−C Plot,tracers, Modified Koval Method, etc. The F−C Plot or flowcapacity-Storage capacity is a static plot where both flow and storagecapacity information are obtained from static log data, permeability,porosity, and thickness. In some embodiments, the F+C plot may be used.The F+C plot is a semi-dynamic plot-constructed similar to an F−C plot,but the flow information is from profile data and the storage capacityinformation is from porosity logs. In some embodiments, the F-Phi plotmay be used. The F-Phi plot is a dynamic plot and both flow and storageinformation are from dynamic production data, for example, tracers orModified Koval Method. Indeed, in some embodiments, determining the newresidence time distribution for each zone of the plurality of zonesincludes using updated tracer data, CRM zonal, Modified Koval Method, anF−C plot, F+C plot, F-Phi plot, log data, ILT data, CRM generated PLT,or any combination thereof. In the running example, the following newresidence time distributions are determined for the zones after thefirst treatment.

Original residence time distribution after treatment 1 or beforetreatment 2 Residence time Residence time distribution using CRMdistribution using CRM Zonal, F-C, Tracers or Reservoir Zonal, F-C,Tracers, etc. Modified Koval Method etc. Zones tD, Dimensionless t, days5 0.17 7 2 0.24 10 4 0.33 13 1 1.07 43 3 9.38 375

At 1920, at least one zone of the plurality of zones may be identifiedto be treated with a conformance agent (second treatment) by comparingthe determined new residence time distributions (after first treatment)to a residence time distribution threshold. In the running example, thethreshold for residence time distribution for treatment 2 is still 10days and any zone with residence time distribution of less than or equalto the threshold may be identified for conformance control for secondtreatment, which would likely shut off that zone. The threshold may bereceived from a user or automatically generated. The threshold for thissecond conformance control treatment analysis may be the same ordifferent than the threshold used for the first conformance controltreatment analysis. In the running example, zones 5 and 2 have residencetimes of 7 and 10 days, respectively, after the first treatment which islower than or equal to the threshold of 10 days. Thus, zones 5 and 2 areidentified for treatment with a conformance agent (treatment 2). Aresidence time of 7 and 10 days indicates that the displacing fluid,such as water or brine from a waterflood operation, breaks through inabout 10 days in the slowest of the identified layers (zone 2) which isequal to the threshold of 10 days.

At 1922, breakthrough time of slowest identified zone may be determinedafter first treatment. The breakthrough time may be determined from theresidence time distributions determined at 1918 for the correspondingidentified zones after treatment 1. In the running example, zones 5 and2 were identified and the residence time distribution is 7 days for zone5 and 10 days for zone 2 so the breakthrough time of slowest identifiedzone is determined to be 10 days.

At 1924, a second conformance control treatment may be recommended atthe determined breakthrough time for the slowest zone that is identifiedafter the first treatment. In the running example, a second conformancecontrol treatment (treatment 2) may be recommended at 10 days for zones5 and 2 after the first conformance treatment, as illustrated below.

Treatment 2 Time of conformance treatment is equal to breakthrough 10days of the slowest of the zones to be shut off (days) Slug Size (bbls)is half of the injected volume before 8308 bbls breakthrough of thefastest zone Resistance factor to be used to reduce its velocity to 171/10th of its original velocity Concentration of the conformance agentto be used based 20776 ppm on the rheology Sweep efficiency aftertreatment 2 @ 1 PV injected 0.6

At 1926, a recommended slug size for the second conformance controltreatment may be determined. The slug size for the second conformancecontrol treatment is determined by calculating injected volume for aperiod of time which is not more than 50% of an updated breakthroughtime for the fastest identified zone. In the running example, thefastest of the zones identified is zone 5 and a second conformancetreatment slug size (treatment 2) is recommended to be 8308 Bbls.

At 1928, a recommended concentration of the conformance agent fortreatment 2 may be determined. The concentration of the conformanceagent for the second conformance control treatment is based on aresistance factor of the conformance agent for the second conformancecontrol treatment and a rheology of the conformance agent for the secondconformance control treatment. The resistance factor of the conformanceagent of the second conformance control treatment is sufficient toreduce updated velocity of the fastest identified zone after the firstconformance control treatment by a factor of about 10. The conformanceagent rheology may be used to determine the concentration after thesufficient resistance factor is calculated. In the running example, theresistance factor for the second conformance agent (treatment 2) isrecommended to be 17. From the rheology for the conformance agent, therecommended conformance agent concentration is determined to be 20776ppm so that a resistance factor of 17 is achieved.

Those of ordinary skill in the art may appreciate that these embodimentsmay lead to an increase in accuracy and an increase in repeatability inthe area of conformance control. For example, a user may be given arecommendation as to the zone, slug size, concentration, and/or timeperiod for the conformance control treatment, and therefore, decisionsmay be based on data and not ad-hoc.

Comparing Different Flood Operations

Different flood operations are oftentimes performed on a hydrocarbonreservoir. For example, a flood operation with polymer, also referred toas polymer flooding, is an enhanced oil recovery technique. In a polymerflooding operation, certain polymers and/or surfactants may be dissolvedin the displacing fluid (e.g., prior to injecting the injection fluid)to decrease the displacing fluid's mobility and increase the displacingfluid's viscosity. By doing so, the displacing fluid flows through thehydrocarbon reservoir in a slower manner in a polymer flood operationand increases the likelihood that a larger volume of the hydrocarbonreservoir will be contacted as compared to a waterflood operation.Analysis of multiple flood operations may be useful in evaluatingproject feasibility and for other purposes.

FIG. 20 illustrates one embodiment of a computer implemented method ofanalyzing at least a first flood operation and a second flood operationon a hydrocarbon reservoir having at least one production well and atleast one injection well, referred to herein as method 2000. The method200 can be executed by a computing system, such as the computing system200 of FIG. 2A or the CRM application 214 thereof. After executing themethod 2000 of FIG. 20, the resulting change in sweep efficiency betweenthe first flood operation (e.g., a waterflood operation) and the secondflood operation (e.g., a polymer flood operation) may be used as inputto update or integrate with the polygon application 216, the zonalapplication 218, the petrotechnical mapping application 220, the one ormore other application 221, the flooding analysis application 212,and/or other item (e.g., as illustrated in FIGS. 2A, 2C). The change insweep efficiency between the first flood operation (e.g., the waterfloodoperation) and the second flood operation (e.g., the polymer floodoperation) may also be used to make physical adjustments, such asphysical adjustments to the polymer flood operation like changing thepolymer concentration, changing the injection rate, and other physicaladjustments.

Starting with the first flood operation, at 2002, production data may bereceived for at least one production well and injection data for atleast one injection well for the first flood operation. A runningexample will be discussed that assumes a hydrocarbon reservoir with afirst injection well I₁ and six production wells P₁, P₂, P₃, P₄, P₅, P₆,a first flood operation (e.g., a waterflood operation) was performed onthe hydrocarbon reservoir between years 1980-1990, and a second floodoperation (e.g., a polymer flood operation) was performed on thehydrocarbon reservoir between years 1990-1995. Production data such asproduction rate data may be received for the six production wells P₁,P₂, P₃, P₄, P₅, P₆ from the first flood operation, for example,production rate data for the entire or a portion of the first floodoperation. Injection data such as injection rate data may be receivedfor the first injection well I_(i) from the first flood operation, forexample, injection rate data for the entire or a portion of the firstflood operation. The production data may include production rate andflowing pressure data as a function of time for production wells, andthe injection data may include injection rate and flowing pressure dataas a function of time for injection wells.

At 2004, CRM may be run using the received production data and thereceived injection data for the first flood operation to generate aresponse time and an interwell connectivity per injection well andproduction well pair for the first flood operation. For example, theresponse time injection well and production well pair may be generatedby CRM using the following equation:

$\tau_{ij} = \frac{c_{t}V_{pij}}{J_{ij}}$In the equation above, V_(pij) represents pore volume of a particularinjection well and production well pair, _(i) represents an injectionwell, _(j) represents an production well, c_(t) representscompressibility as function of time, J_(ij) represents the productivityindex for a particular injection well and production well pair, and Tauor τ_(ij) represents the response time of a particular injection welland production well pair.

Also, the interwell connectivity per injection well and production wellpair for the first flood operation may be generated by CRM using thefollowing equation:

$f_{ij} = \frac{q_{ij}}{I_{i}}$In the equation above, q_(ij) represents the total flow rate for aparticular injection well and production well pair, I_(i) represents thetotal flow rate for a particular injection well, _(i) represents aninjection well, _(j) represents an production well, and f_(ij)represents the interwell connectivity between a particular injectionwell and production well pair.

The productivity index J_(ij) between a particular injection well andproduction well pair may be calculated as follows:

$J_{ij} = \frac{I_{i}f_{ij}}{\Delta\; P_{ij}}$In the equation above, ΔP_(ij) represents the pressure differencebetween a particular injection well and a production well and the otherterms are discussed hereinabove.

CRM may be run with or without allowed well connections as describedherein. CRM may generate the response times and interwell connectivitiesfor the first injection well I₁ and the six production wells drilledinto the hydrocarbon reservoir. Alternatively, if this example includedhundreds of wells in addition to the first injection well I₁ and the sixproduction wells drilled into the reservoir, CRM may generate responsetimes and interwell connectivities for the first flood operation for allinjection well and production well pairs and the generated responsetimes and interwell connectivities for the well pairs of interest may beused at 2006.

The following table A illustrates the generated response times in thesecond column, steady state interwell connectivities in the third columnfor an infinite period of time (fij), and transient interwellconnectivities in the fifth column for a specified period of time (f*ij)for Inj₁ and P₁, P₂, P₃, P₄, P₅, P₆ for the first flood operation:

TABLE A Injection well Inj₁ and production wells P₁₋P₆ - first floodoperation Equation Equation results with results with transient f*ijResponse steady state fij f*ij -interwell Swept Pore time (τ) fij-interwell Swept Pore connectivity Volume Proxy Response connectivityVolume Proxy (max duration (max duration Producers time Proxy (steadystate) (steady state) of the flood) of the flood) P₁ 10 0.30 153,8460.30 153,846 P₂ 10 0.20 106,667 0.20 106,667 P₃ 10 0.20 95,238 0.2095,238 P₄ 50 0.10 246,914 0.09 233,053 P₅ 20 0.10 111,111 0.10 111,028P₆ 80 0.10 410,256 0.08 342,441 180 Total swept 1,124,032 1,042,274 Porevolume For Inj₁ Lorenz .44 Coefficient For first flood operation

At 2006, the generated response times, the generated interwellconnectivities, the received production data, the received injectiondata, or any combination thereof may be used to generate a proxy of porevolume swept per injection well and production well pair for the firstflood operation. Disclosed herein are three methodologies to generatethe proxy of pore volume swept per injection well and production wellpair for the first flood operation. Each of the three methodologies willbe discussed, but those of ordinary skill in the art will appreciatethat all three methodologies are not necessary. For example, the secondmethodology only may be used throughout. As another example, the thirdmethodology only may be used throughout.

The first methodology to generate a proxy of pore volume swept for aparticular injection well and production well pair is to treat thegenerated response time for the particular pair as the proxy of porevolume swept. Thus, using the generated response time, the proxy forInj₁ and P₁ is 10, the proxy for Inj₁ and P₂ is 10, the proxy for Inj₁and P₃ is 10, the proxy for Inj₁ and P₄ is 50, the proxy for Inj₁ and P₅is 20, and the proxy for Inj₁ and P₆ is 80.

The second methodology to generate a proxy of pore volume swept for aparticular injection well and production well pair is to use thefollowing equation with steady state interwell connectivities:

$V_{pij} = \frac{\tau_{ij}f_{ij}I_{i}}{c_{t}\Delta\; P_{ij}}$In the equation above, V_(pij) represents pore volume of a particularinjection well and production well pair, c_(t) representscompressibility as a function of time, f_(ij) represents the interwellconnectivity for an injection well and production well pair, Tau orτ_(ij) represents the response time of a particular injection well andproduction well pair, ΔP_(ij) represents the pressure difference betweena particular injection well and a production well, and I_(i) representsthe total flow rate for a particular injection well. The above equationfor V_(pij) is a combination of the J_(ij) and τ_(ij) equations. Thus,using the equation with steady state interwell connectivity, the proxyfor Inj₁ and P₁ is 153,846, the proxy for Inj₁ and P₂ is 106,667, theproxy for Inj₁ and P₃ is 95,238, the proxy for Inj₁ and P₄ is 246,914,the proxy for Inj₁ and P₅ is 111,111, and the proxy for Inj₁ and P₆ is410,256.

The third methodology to generate a proxy of pore volume swept for aparticular injection well and production well pair is to use thefollowing equation with transient interwell connectivities:

$V_{pij} = \frac{\tau_{ij}f_{ij}^{*}I_{i}}{c_{t}\Delta\; P_{ij}}$In the equation above, the main difference is the use of f*_(ij) torepresent a transient interwell connectivity for an injection well andproduction well pair. For the running example, 36 months was used forthe transient interwell connectivities. Thus, using the equation withtransient interwell connectivity, the proxy for Inj₁ and P₁ is 153,846,the proxy for Inj₁ and P₂ is 106,667, the proxy for Inj₁ and P₃ is95,238, the proxy for Inj₁ and P₄ is 233,053, the proxy for Inj₁ and P₅is 111,028, and the proxy for Inj₁ and P₆ is 342,441.

At 2008, the generated proxies of pore volume swept per injection welland production well pair for the first flood operation may be aggregatedto generate an estimate of pore volume swept at a well level, at areservoir level, or both for the first flood operation. The generatedproxies may be aggregated using addition. The estimate of pore volumeswept at the well level may be for a particular injection well or for aparticular production well. Returning to the running example, theestimate of pore volume swept at the well level for Inj₁ based on thegenerated response time is 180. The estimate of pore volume swept at thewell level for Inj₁ based on the equation with steady state interwellconductivities is 1,124,032. The estimate of pore volume swept at thewell level for Inj₁ based on the equation with transient interwellconductivities is 1,042,274.

At 2010, heterogeneity at the well level, the reservoir level, or bothmay be determined for the first flood operation (e.g., by using thegenerated response times and the generated interwell connectivities).The heterogeneity may be determined to verify the accuracy of a changein sweep efficiency, discussed further at 2026. Heterogeneity of thefirst flood operation may be calculating using a Lorenz coefficient(Lc), Koval, a flow capacity-storage capacity curve, any combinationthereof, etc. In some embodiments, the heterogeneity may be determinedby quantifying the area below the flow capacity-storage capacity curvefor the first flood operation. Heterogeneity is discussed further inU.S. Pat. No. 8,428,924, which is incorporated herein by reference inits entirety. In the running example, the heterogeneity at the welllevel for the first flood operation is 0.44182, as illustrated in FIG.21A.

At 2012, 2014, 2016, 2018, 2020, a similar approach may be used for thesecond flood operation, as in Table B. Returning to the running example,the estimate of pore volume swept at the well level for the second floodoperation based on the generated response time for Inj₁ is 275. Theestimate of pore volume swept at the well level for the second floodoperation based on the equation with steady state interwellconductivities for Inj₁ is 1,199,319. The estimate of pore volume sweptat the well level for the second flood operation based on the equationwith transient interwell conductivities for Inj₁ is 1,135,528. Theheterogeneity at the well level for the second flood operation is0.1182, as illustrated in FIG. 21B.

TABLE B Injection well Inj₁ and production wells P₁₋P₆ - second floodoperation Equation Equation results with f*ij Response results with fijf*ij -interwell Swept Pore time (τ) fij -interwell Swept Poreconnectivity Volume Proxy Response connectivity VolumeProxy (maxduration (max duration Producers time Proxy (steady state) (steadystate) of the flood) of the flood) P₁ 40 0.20 208,333 0.19 202,641 P₂ 500.10 134,409 0.09 126,864 P₃ 50 0.10 122,549 0.09 115,670 P₄ 35 0.22194,444 0.22 191,268 P₅ 40 0.12 133,333 0.12 129,690 P₆ 60 0.26 406,2500.24 369,396 275 Total swept 1,199,319 1,135,528 Pore volume For Inj₁Lorenz .12 Coefficient for second flood operation Positive change  7%Positive change 9% in volumetric in volumetric sweep sweep (steadystate) (max duration of the flood) Lorenz 73% coefficient reduction

At 2022, the generated estimate of pore volume swept for the first floodoperation and the generated estimate of pore volume swept for the secondflood operation may be compared to determine a change in sweepefficiency at the well level, at the reservoir level, or both. Thechange in sweep efficiency may be determined by calculating thedifference between the estimate of pore volume swept for the secondflood operation and the estimate of pore volume swept for the firstflood operation. The determined difference is divided by the estimate ofpore volume swept for the first flood operation. Returning to therunning example, if the first methodology was used, then the change insweep efficiency for Inj₁ is about 53% (i.e.,(275−180)/180=0.5277*100=53%), which is a positive change in volumetricsweep. If the second methodology was used, then the change in sweepefficiency for Inj₁ is about 7% (i.e.,(1,199,319−1,124,032)/1,124,032=0.0669*100=7%), which is a positivechange in volumetric sweep (steady state). If the third methodology wasused, then the change in sweep efficiency for Inj₁ is about 9% (i.e.,(1,135,528−1,042,274)/1,042,274=0.0894*100=9%), which is a positivechange in volumetric sweep (max duration of the flood).

At 2024, the determined heterogeneity of the first flood operation andthe determined heterogeneity of the second flood operation may becompared to determine a change in heterogeneity at the well level, atthe reservoir level, or both. The comparison at 2022 may be performedbefore the comparison at 2024 or the comparison at 2024 may be performedbefore the comparison at 2022. Nonetheless, the change in heterogeneitymay be determined by calculating the difference between the determinedheterogeneity for the second flood operation and the determinedheterogeneity for the first flood operation. The determined differenceis divided by the determined heterogeneity for the first floodoperation. Returning to the running example, at the well level, theheterogeneity for Inj₁ is about 73% (i.e.,(0.44−0.12)/0.44=0.7272*100=73%) as illustrated in FIG. 21C, which is aLorenz coefficient reduction.

At 2026, the determined change in heterogeneity and the determinedchange in sweep efficiency may be compared to verify accuracy of thedetermined change in sweep efficiency. In the running example, aninverse relationship may indicate that the determined change in sweepefficiency, regardless of the methodology used, is likely accurate. Forinstance, a positive change in volumetric sweep is consistent with areduction in heterogeneity, and therefore a positive increase in 53%,7%, or 9% is consistent with a reduction of 73% in heterogeneity.

Those of ordinary skill in the art will appreciate that variousmodifications may be made to the illustrated embodiments. For example,CRM may be run at 2004 and/or 2014 using allowed well connections asinput, as discussed herein. The allowed well connections may bedetermined, and the running of the capacitance resistance modeling forthe first flood operation, the second flood operation, or both includesusing the determined allowed connections as an input to the capacitanceresistance modeling.

Moreover, the first flood operation may be practically any floodoperation and the second flood operation may be practically any floodoperation, and the embodiments disclosed herein may be used to analyzethem. For example, the first flood operation may be a polymer floodoperation using a polymer at a first concentration, and the second floodoperation may be a polymer flood operation using a polymer at a secondconcentration. Alternatively, the first flood operation may be awaterflood operation like in the running example, but the second floodoperation may be a polymer flood operation with a surfactant and apolymer. Alternatively, the first flood operation may be a waterfloodoperation that uses a first brine and the second flood operation may bea waterflood operation that uses a second brine. The first brine and thesecond brine may be different, or the first brine and the second brinemay be substantially similar in some embodiments.

Furthermore, the running example assumed one injection well forsimplicity, but a change in sweep efficiency may be generated asdescribed above for a plurality of injection wells. Indeed, the runningexample may include two injections wells, as illustrated further inFIGS. 22A-22B. Also, the embodiments discussed above can be applied atthe reservoir level, as illustrated in FIGS. 23A-23G. Furthermore, thereservoir may include a plurality of zones, and each zone may be treatedas an injection well, as illustrated in FIGS. 24A-24F.

Optionally, if a flood operation uses a polymer alone or a polymer incombination with other material (e.g., surfactant), then the rheology ofthe polymer may be accounted for in CRM. In the running example, thesecond flood operation is a polymer flood operation, and therefore, therheology of the polymer could have been accounted for in the running ofCRM at 2014. Polymers used in oil fields oftentimes exhibit shearthinning rheology. Shear thinning rheology for a polymer oftentimescauses its viscosity to increase away from the well bore area. Theviscosity variation may be sharp between near well bore areas to farwell bore areas, as illustrated in FIG. 25. The polymer rheology can bemathematically described by at least one rheological model such as apower law model, Meters model, Carreau model, etc. based on laboratoryexperiments.

To account for the rheology in running CRM, accounting for the rheologyincludes separating each injection well and production well pair of thepolymer flood operation into three tanks including a near injection welltank, a near production well tank, and a middle tank between the nearinjection well tank and the near production well tan, as illustrated inFIG. 26. By separating into at least three tanks, the rheology near wellbore is separated from the rheology effects away from the well bore.

Furthermore, running the capacitance resistance modeling includes using(i) material balance equations for each of the tanks, (ii) injection andproduction well rates, (iii) injection and production well flowingpressure data, and (iv) the polymer rheology (e.g., using the power lawmodel). The material balance equations are written out for each of thetanks separately as illustrated below. The near injection well tankmaterial balance equation is:

${c_{t}V_{p,i}\frac{\partial{\overset{\_}{P}}_{i}}{\partial t}} = {{i_{i,p}(t)} - {q_{i}(t)}}$In the near injection well tank equation, c_(t) is the totalcompressibility, V_(p,i) is the pore volume of the near injection welltank associated with injection well i, P _(i) is the average pressure inthe near injection well tank associated with injection well i,i_(i,p)(t) is the flow rate of the polymer in injection well i andq_(i)(t) is flow rate of the fluid out of the near injection well tankassociated with the injection well i.

The near production well tank material balance equation is:

${c_{t}V_{p,i}\frac{\partial{\overset{\_}{P}}_{j}}{\partial t}} = {{{\overset{\_}{q}}_{i}(t)} - \left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)}$In the near production well tank equation, c_(t) is the totalcompressibility, V_(p,j) is the pore volume of the near production welltank associated with production well j, P _(j) is the average pressurein the near production well tank associated with production well j, q_(j)(t) is the flow rate of the fluid in production well j, q_(p,j)(t)is the polymer flow rate in production well j and q_(o,j)(t) is the oilflow rate in production well j.

The middle tank material balance equation is:

${c_{i}{\overset{\_}{V}}_{p,j}\frac{\partial\overset{\_}{{\overset{\_}{P}}_{j}}}{\partial t}} = {{\sum\limits_{i = 1}^{Nt}{q_{i,j}(t)}} - {{\overset{\_}{q}}_{j}(t)}}$In the middle tank equation, c_(t) is the total compressibility,q_(i,j)(t) is the interwell total flow rate between injection well i andproduction well j, P_(j) is the average pressure in the middle tank, V_(p,j) is the pore volume of the middle tank.

The CRM polymer formulation (a single equation, shown below) is obtainedby the combination of the material balance equations for each of thetanks. The final CRM formulation thus depends (i) material balanceequations for each of the tanks, (ii) injection and production wellrates, (iii) injection and production well flowing pressure data, and(iv) the polymer rheology (e.g., using the power law model). The CRMpolymer formulation is:

${{{\overset{\_}{\tau}}_{j}\tau_{j}\frac{\partial\;}{\partial t}\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + {{\overset{\_}{\tau}}_{j}\frac{\partial\;}{\partial t}\left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} + {\left( {{{\overset{\_}{\tau}}_{j}\frac{{\overset{\_}{J}}_{j}}{J_{j}}} + \tau_{j}} \right)\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + \left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} = {\sum\limits_{i = 1}^{Nt}{f_{i,j}\left( {{i_{i,p}(t)} + {\tau_{i}\left( {\frac{\partial{i_{i,p}^{n}(t)}}{\partial t} - {J_{i}\frac{\partial P_{{wf},i}}{\partial t}}} \right)}} \right)}}$In the CRM polymer formulation, τ_(i) is the time constant for the nearinjection well tank associated with injection well i, τ_(i) is the timeconstant for the near production well tank associated with productionwell j, and τ _(j) is the time constant for the middle tank. J_(i) isthe injectivity index for injection well i, J_(j) is the productivityindex for production well j and J _(j) is the productivity index for themiddle tank. P_(wf,j) is the well flowing pressure for production well jand P_(wf,i) is the well flowing pressure for injection well i.

The output from CRM using the CRM polymer formulation above includes τfor each of the tanks and the interwell connectivities fij or f*ij foreach injection well and production well pair, and CRM therefore alsoaccounts for the rheology of the polymer of the flood operation. Thoseof ordinary skill in the art will appreciate that existing CRM cannotaccount for rheology of polymer, and therefore the embodiments disclosedherein may lead to more accurate CRM output for flood operationsinvolving a polymer. In some embodiments, rheology of a polymer may beaccounted for in running CRM separate from determining sweep efficiencyand/or separate from determining heterogeneity, as illustrated in anembodiment of a method of analyzing a polymer flood operation on ahydrocarbon reservoir in FIG. 27 (e.g., at method 2700, numbers2702-2710).

Those of ordinary skill in the art may appreciate that analysis of afirst flood operation and a second flood operation may have variousbenefits. For example, the injection and production field data of thepolymer flood may be used via CRM to characterize the sweep efficiencyimprovement and performance of the polymer flood on the fly. Thisdevelopment may help with the following: 1. Optimizing a polymer floodwhile in progress by making changes such as adjustinginjection-production rates or bottom hole pressures of wells tore-direct flow to un-swept volumes and improve performance of thepolymer flood operation, 2. Design/optimize operational parameters of apolymer floods based on waterflood operation performance (e.g., chooseoptimal polymer concentrations, optimize injection-production rates andpressures, etc.), 3. Analyze the performance of a past polymer flood(pilot) to determine potential of future implementations, 4. Analyze apolymer flood pilot and use the incremental sweep efficiency informationobtained to design future polymer floods, and/or 5. Determine infilldrilling opportunities for the undergoing polymer flood to increasehydrocarbon recovery. Manipulation of CRM fundamental equations allowscalculation of improved sweep efficiency by polymer floods from CRManalysis of polymer floods and comparison to the correspondingwaterflood CRM performance. Time scale of connection or response time(tau) and well pair connectivities (fij) are used to calculateincremental sweep efficiency of polymer flood. As discussed above, thiseffort is not limited to waterfloods and polymer floods, and forexample, may be applied to surfactant-polymer flood operations. Thiscapability may allow robust design and optimization of chemical enhancedoil recovery (CEOR) processes based on field data. It may also helpimprove the performance of floods on the fly, which can change theoutcome of a project from a failure to a success, potentially savingmillions of dollars.

Pattern Management

Oftentimes, injection wells and production wells are drilled inlocations so as to form patterns. Polygons are sometimes generated torepresent the patterns. The embodiments discussed herein relate to (i)using producer centered (e.g., Voronoi) polygons to help identify infilldrilling locations, (ii) using the polygons in choosing between twoinfill candidates in two different reservoirs, (iii) using the polygons(e.g., streamgrids) for converting an existing production well into aninjection well or vice versa (referred to as pattern realignment, (iv)using polygon (e.g., streamgrid) allocation factors to initiate CRMinterwell connectivity, and/or (v) estimating maximal areal sweep byzone and by reservoir with the populated polygons (e.g., streamgrids).The embodiments can be executed by a computing system, such as thecomputing system 200 of FIG. 2A or the polygon application 216 thereof.As discussed herein, a polygon may be a streamgrid or streamgrid polygonin some embodiments. After executing the embodiments, the output may beused as input to update or integrate with the CRM application 214, thezonal application 218, the petrotechnical mapping application 220, theone or more other application 221, the flooding analysis application212, and/or other item (e.g., as illustrated in FIG.).

FIG. 28A illustrates one embodiment of a method for using producercentered (e.g., Voronoi) polygons to help identify infill drillinglocations, referred to herein as method 2800. The method 2800 includes(i) loading or receiving well locations, reservoir boundary, andinjection and production rate histories, (ii) creating producer-centered(e.g., Voronoi) polygons based on the producer locations and thereservoir boundary (or any fault, etc.), (iii) calculating the area (Ai)(e.g., drainage area, pore volume, proxy of OOIP, etc.) of any givenproducer by each polygon associated with each producer based on geometryor the geological boundary of the polygon, (iv) for each producer,calculating cumulative oil production (Qi) and a ratio between Qi andAi, (v) ranking the producers from the smallest to largest Q/A ratio,and/or (vi) locating or indicating infill drilling places in thepolygons with high-ranking producers. FIG. 28B and the following tableillustrate an example.

Area of producer- Cumulative Oil centered Production from Ratio Producerpolygon (A) each producer (Q) (Q/A) Rank 1 35 1269 36.3 2 2 23 6389277.8 7 3 82 8929 108.9 4 4 22 9827 446.7 9 5 57 6492 113.9 5 6 33 44613.5 1 7 15 8921 594.7 10 8 24 8956 373.2 8 9 45 8013 178.1 6 10 45 164336.5 3

FIG. 29A illustrates one embodiment of a computer implemented method forusing polygons to choose between two infill candidates in two differentreservoirs, referred to herein as the method 2900. The method 2900includes (i) loading or receiving well locations, reservoir boundary,and injection and production rate histories, (ii) creatingproducer-centered (e.g., Voronoi) polygons based on the producerlocations and the reservoir boundary (or any fault, etc.), (iii)calculating the area (A_(i)) (e.g., drainage area, pore volume, proxy ofOOIP, etc.) covered by each polygon associated with each producer basedon geometry (or the geological boundary) of the polygon, (iv) for eachproducer, calculating cumulative oil production (Qi) and rank producersfrom largest Q to smallest Q, (v) for each producer, calculatingcumulative oil production (Qi) and a ratio between Qi and Ai, (vi)ranking the producers from the smallest to largest Q/A ratio, (vii)calculating a norm area (or Pore volume) and norm cumulative oilproduction in that reservoir, and/or (viii) calculating an index ofuneven sweep (IUS) of the reservoir. The method 2900 further includes(viiii) repeating (i)-(viii) for another reservoir, and ranking thereservoir for infill drilling opportunity with the largest IUS. FIGS.29B-29C and the following table illustrate an example.

Area (or Pore Volume) of Cumulative Norm Producer- Oil Production Area(or Norm centered from each Ratio Pore Cum Producer Polygon (A) producer(Q) (Q/A) Rank Volume) Oil Reservoir 1 7 15 8921 594.7 1 0.04 0.150.002884 4 22 9827 446.7 2 0.10 0.31 0.016005 8 24 8956 373.2 3 0.160.46 0.040035 2 23 6389 277.8 4 0.22 0.56 0.07067 9 45 8013 178.1 5 0.340.69 0.144579 5 57 6492 113.9 6 0.49 0.80 0.256018 3 82 8929 108.9 70.70 0.94 0.443589 10  45 1643 36.5 8 0.82 0.97 0.556779 1 35 1269 36.39 0.91 0.99 0.647012 6 33 446 13.5 10 1.00 1.00 0.733309 sum 381 60885IUS 0.467 Reservoir 2 9 38 6522 171.6 1 0.05 0.12 0.003021 2 57 7304128.1 2 0.13 0.25 0.017157 5 68 8452 124.3 3 0.22 0.41 0.04708 7 83 9381113.0 4 0.33 0.58 0.101643 6 57 4921 86.3 5 0.40 0.67 0.149051 4 87 568865.4 6 0.52 0.78 0.232659 10  45 2786 61.9 7 0.58 0.83 0.280552 8 794218 53.4 8 0.68 0.90 0.371375 3 44 1697 38.6 9 0.74 0.94 0.425132 12 44 1000 22.7 10 0.80 0.95 0.480336 1 79 1747 22.1 11 0.90 0.99 0.58209611  72 764 10.6 12 1.00 1.00 0.677043 Sum 753 54480 IUS 0.354

FIG. 30A illustrates one embodiment of a computer implemented method forusing polygons (e.g., streamgrids) for pattern realignment, referred toherein as the method 3000. The method 2800 includes (i) loading orreceiving well locations, and injection and production rate historiesfor all wells, (ii) creating polygons (e.g., streamgrid polygons) basedon the well locations, (iii) calculating allocation factors for injectorand producers based on any available allocation method (e.g., injectionangle, producer angle), (iv) calculating the allocated water injectionand water production within each polygon (e.g., streamgrid) (betweenconnected injector-producer pair), (v) calculating water cycling betweenconnected injector-producer pair for each polygon (e.g., streamgrid),(vi) defining a threshold for water cycling based on a distribution,and/or (vii) identifying at least one polygon (e.g., streamgrid) thathave water cycling above the threshold and convert the producer in thatpolygon (e.g., streamgrid) for pattern realignment. FIGS. 30B-30Cillustrate an example.

FIG. 31A illustrates one embodiment of a computer implemented method forusing polygons (e.g., streamgrid) allocation factors to initiate CRMinterwell connectivity, referred to herein as the method 3100. Themethod 3100 includes (i) loading or receiving well locations, andinjection and production rate histories for all wells, (ii) creatingpolygons (e.g., streamgrid) based on the well locations, (iii)calculating the allocation factors for injector based on any availableallocation method (e.g. injection angle, number of producers, andInjector Area, etc.), and/or (iv) exporting those allocation factors asinitial values of interwell connectivity between well pairs in CRM.FIGS. 31B-31C illustrate an example.

FIG. 32A illustrates one embodiment of a computer implemented method forestimating maximal areal sweep by zone and by reservoir with thepopulated polygons (e.g., streamgrids), referred to herein as the method3200. The method 3200 includes (i) for any given zone of a reservoir,getting a reservoir boundary in that zone and calculating its total area(St), (ii) getting contact point of all wells that penetrate and areperforated in that zone, (iii) creating streamgrid with the well-zonecontact locations, (iv) calculating the total area (S) covered bypopulated streamgrids, and/or (v) estimating maximal areal sweepefficiency in that zone, which equals S/St. The method 3200 alsoincludes repeating (i)-(v) for all zones in a reservoir and getting theS and St for each zone, calculate the ratio between summation of S fromall zones and summation of St from all zones to get the maximum arealsweep efficiency for the reservoir. FIGS. 32B-32C illustrate an example.

Value of Injected Fluid (VOIF)

An embodiment of a computer implemented method of determining a value ofinjected fluid for a flood operation on a hydrocarbon reservoir havingat least one injection well and at least one production well, referredto as 3300 in FIG. 33. The method 3300 can be executed by a computingsystem, such as the computing system 200 of FIG. 2A or the CRMapplication 214 thereof. After executing the method 2000 of FIG. 20, theresulting output may be used as input to update or integrate with thepolygon application 216, the zonal application 218, the petrotechnicalmapping application 220, the one or more other application 221, theflooding analysis application 212, and/or other item (e.g., FIGS. 2A,2C). For ease of understanding, a running example will be used and therunning example assumes five injection wells and four production wells.

At 3302, the method 3300, for a first injection well, receivinginjection rate data for the first injection well and production ratedata for at least one production well communicating with the injectionwell. At 3304, the method 3300 includes running capacitance resistancemodeling using the received data to generate an interwell connectivityfor each injection well and production well pair. CRM may be run usingallowed well connections as described herein. The table belowillustrates the interwell connectivities:

Connectivity Values CRM Parameters for Total Production Estimation Inj.1 Inj. 2 Inj. 3 Inj. 4 Inj. 5 q(t = t0), qt_j Error, τ (f1j) (f2j) (f3j)(f4j) (f5j) Jj B/D B/D Pro. 1 365 0.3 0.2 0.5 0.25 0.25 0 317761.14295976 Pro. 2 60 0.4 0.4 0 0.25 0.25 0 199 41.56255395 Pro. 3 1800.3 0.1 0.25 0.25 0.1 0 187 28.98366241 Pro. 4 180 0 0.1 0.25 0.25 0.250 2333 33.53199791 Sum fij = <1 1 0.8 1 1 0.85 41.30529351 Injection0.925 Efficiency: Target VRR: 1.081

At 3306, the method 3300 includes determining a value of injected fluidfor each injection well and production well pair using the generatedinterwell connectivity for the well pair and an oil-cut value for theproduction well of the pair. In some embodiments, determining the valueof injected fluid per well pair includes using an equation for each wellpair, wherein the equation is:VOIFij=f _(ij) f _(oj)In the equation, f_(oj) is oil cut and f_(ij) is interwell connectivity.In some embodiments, the f_(ij) may be at a steady state as discussedherein. Alternatively, if transient is desired, then fij may be replacedwith f*ij in the equation.

At 3308, the method 3300 includes aggregating the generated values ofinjected fluid per pair to determine a value of injected fluid for thefirst injection well. In the running example, the final value ofinjected fluid for the first injection well is 0.37 at steady state byaggregating the four values of injected fluid per well pair(0.15+0.16+0.06+0=0.37).

At 3310, the method 3300 includes generating a value of injected fluidfor at least one other injection well. At 3312, the method 3300 includesranking the first injection well and the at least one other injectionwell based on the values of injected fluid. Returning to the runningexample, at the steady state, the ranking and intermediate items may bethe following:

Steady State Connectivities VOIF Table Steady State Connectivities Time1E+13 days Last fij Steady Year Oil- State Inj. 1 Inj. 2 Inj. 3 Inj. 4Inj. 5 Oil cut Cut j Pro. 1 0.30 0.20 0.50 0.25 0.25 Pro. 2 0.40 0.400.00 0.25 0.25 Pro. 1 0.5 Pro. 3 0.30 0.10 0.25 0.25 0.10 Pro. 2 0.4Pro. 4 0.00 0.10 0.25 0.25 0.25 Pro. 3 0.2 Pro. 4 0.2 Sum fij 1 0.8 1 10.85

Calculate VOIF for each wellpair - Sum for Injectors, Rank Injectors ForOne Barrel Table Steady State Value of Injected Fluid (VOIF) Evaluationfor 1 bbl of injection 1 1 1 1 1 VOIF*_ij Inj. 1 Inj. 2 Inj. 3 Inj. 4Inj. 5 Pro. 1 0.15 0.1 0.25 0.125 0.125 Pro. 2 0.16 0.16 0 0.1 0.1 Pro.3 0.06 0.02 0.05 0.05 0.02 Pro. 4 0 0.02 0.05 0.05 0.05 VOIF_i 0.37 0.30.35 0.325 0.295 Steady State Rank 1 4 2 3 5

At 3314, the method 3300 may include generating a net value of injectedfluid for the first injection well and the at least one other injectionwell. The net value for a particular injection well may be determinesusing the following equations:Net VOIF_(i)=Σ_(j) ^(N) f _(ij) I _(i) f _(oj) OR Net VOIF_(ij) =f _(ij)I _(i) f _(oj)

At 3316, the method 3300 includes reranking the first injection well andthe at least one other injection well based the net values of injectedfluid. In the running example, for steady state, the reranking may be:

Calculate Net Value of Injection - Steady State For Current InjectionSteady State Net Value of Injected Fluid (VOIF) Evaluation CurrentInj_i, B/D 1000 500 1000 500 1000 Net VOIF_ij Inj. 1 Inj. 2 Inj. 3 Inj.4 Inj. 5 Pro. 1 150 50 250 62.5 125 Pro. 2 160 80 0 50 100 Pro. 3 60 1050 25 20 Pro. 4 0 10 50 25 50 Net VOIF_i 370 150 350 162.5 295 Rank 1 52 4 3

In the running example, assuming transient, the following tablesillustrate the items that may be generated:

Transient Connectivities VOIF Table 2 B 6 month Time 180 days Last YearOil Oil- fij Transient Inj. 1 Inj. 2 Inj. 3 Inj. 4 Inj. 5 cut Cut j Pro.1 0.12 0.08 0.19 0.10 0.10 Pro. 1 0.5 Pro. 2 0.38 0.38 0.00 0.24 0.24Pro. 2 0.4 Pro. 3 0.19 0.06 0.16 0.16 0.06 Pro. 3 0.2 Pro. 4 0.00 0.060.16 0.16 0.16 Pro. 4 0.2 Sum Transient fij 0.69 0.58 0.51 0.65 0.56

Calculate VOIF for each well pair - Sum for Injectors, Rank InjectorsFor One Barrel Table Transient Value of Injected Fluid (VOIF) Evaluationfor 1 bbl of injection 1 1 1 1 1 VOIF*_ij Inj. 1 Inj. 2 Inj. 3 Inj. 4Inj. 5 Pro. 1 0.06 0.04 0.10 0.05 0.05 Pro. 2 0.15 0.15 0.00 0.10 0.10Pro. 3 0.04 0.01 0.03 0.03 0.01 Pro. 4 0.00 0.01 0.03 0.03 0.03 VOIF_i0.25 0.22 0.16 0.21 0.19 Transient Rank 1 2 5 3 4

Calculate VOIF for each well pair - Sum for Injectors, Rank InjectorsFor Current Injection Transient Net Value of Injected Fluid (VOIF)Evaluation Current Inj_i, B/D 1000 500 1000 500 1000 Net VOIF_ij Inj. 1Inj. 2 Inj. 3 Inj. 4 Inj. 5 Pro. 1 58 19 97 24 49 Pro. 2 152 76 0 48 95Pro. 3 38 6 32 16 13 Pro. 4 0 6 32 16 32 Net VOIF_i 248 108 161 103 188Rank 1 4 3 5 2

Those of ordinary skill in the art will appreciate that modificationsmay be made to the illustrated embodiments. For example, the hydrocarbonreservoir may include a plurality of zones, and each zone is treated asan injection well as described above. Indeed, VOIF may be determined ata well level or at a zonal level. Furthermore, sensitivity analysis maybe used. For example, (i) sensitivity analysis may be performed todetermine the allowed well connections for CRM, (ii) CRM may be run withsensitivity analysis, (iii) VOIF may be determined for an injection wellat the steady state with or without sensitivity analysis, and/or (iv)VOIF may be determined for an injection well as transient with orwithout sensitivity analysis. VOIF may also be used to recommend atarget injection rate. VOIF may also be used to recommend a conformancecandidate (e.g., if f*ij or fij is more than a threshold such as 0.4,and if VOIF is less than a threshold such as 0.1, and if injection rateis more than average, then the method 3300 may recommend thecorresponding well and/or zone as a conformance candidate). Criteria mayalso be used to recommend a stimulation candidate. Thus, at 3318, themethod 3300 may generate a recommendation. Also, in some embodiments,PLT can be a substitute for oil-cut in determining VOIF.

CRM Zonal

CRM General Formulation: In one example, a representation of a produceris interacting with offset injectors in which a fraction of eachinjector injection rate (f_(ij)) is contributing to the production rateof producer j. Because CRM is derived based on the continuity equation,all the model parameters reflect physical characteristics of thereservoir. Interwell connectivities (f_(ij)), delay time constants(τ_(ij)), and productivity indices (J_(ij)) between any injector i (i=1,2, . . . , N_(inj)) and producer j (j=1, 2, . . . , N_(pro)) are CRMmodel parameters and are back-calculated during the course ofsimultaneous fitting of Eq. 1 to the fluid production of all producers.CRM formulation is based on producer control volume and can be writtenas:

$\begin{matrix}{{q_{j}\left( t_{n} \right)} = {{{q_{j}\left( t_{0} \right)}e^{- {(\frac{t_{n} - t_{0}}{\tau_{j}})}}} + {\sum\limits_{k = 1}^{n}{{e^{- {(\frac{t_{n} - t_{0}}{\tau_{j}})}}\left( {1 - e^{\frac{{- \Delta}\; t_{k}}{\tau_{j}}}} \right)}\left( {{\sum\limits_{i = 1}^{N_{inj}}\left\lbrack {f_{ij}I_{i}^{(k)}} \right\rbrack} - {J_{j}\tau_{j}\frac{\Delta\; p_{{wf},j}^{(k)}}{\Delta\; t_{k}}}} \right)\left( {{{{for}\mspace{14mu} j} = 1},2,\ldots\;,N_{pro}} \right)}}}} & (1)\end{matrix}$Where I_(i) ^((k)) and Δp_(wf,i) ^((k)) represent the rate of injector iand changes in flowing bottomhole pressure of producer j during timeinterval Δt_(k)=t_(k)−t_(k-1), respectively.

As Eq. 1 illustrates, fluid production rate at producer j is composed ofthree elements: primary depletion the first term, the impact ofinjection input signals, and the variation caused by changing thebottomhole pressure of producer j.

CRM Zonal and its application in generating continuous productionprofile over time: Zonal connectivity can be identified by CRM using ILTdata. Injection profile data obtained over time can be used to splitinjection to the zonal level. Assuming no vertical communication betweendifferent zones, and using the injection profile and injection rateseach injector gets split into multiple zonal level injectors. The Tablebelow illustrates the breakdown of injection rate from one injector (I1)over time into two zones (Z1 and Z2) as an example.

Injection Zonal split Zonal Injection Profile from ILT (%) RateInjection ILT I1- ILT I1- Injection - Injection - Date Rate I1 Z1 Z2I1Z1 I1Z2 1 Jan. 1, 2000 4490 41 59 1840.9 2649.1 2 Feb. 1, 2000 1090 4555 490.5 599.5 3 Mar. 1, 2000 2530 20 80 506 2024 4 Apr. 1, 2000 2950 928 2714 236 5 May 1, 2000 2050 51 49 1045.5 1004.5 6 Jun. 1, 2000 4040 919 3676.4 363.6 7 Jul. 1, 2000 3010 32 68 963.2 2046.8 8 Aug. 1, 20003640 2 98 72.8 3567.2 9 Sep. 1, 2000 2960 67 33 1983.2 976.8 10 Oct. 1,2000 3230 73 27 2357.9 872.1 11 Nov. 1, 2000 4420 53 47 2342.6 2077.4 12Dec. 1, 2000 2290 44 56 1007.6 1282.4 13 Jan. 1, 2001 4080 4 96 163.23916.8 14 Feb. 1, 2001 4880 56 44 2732.8 2147.2 15 Mar. 1, 2001 2280 3367 752.4 1527.6 16 Apr. 1, 2001 1980 61 39 1207.8 772.2 17 May 1, 20014710 6 94 282.6 4427.4 18 Jun. 1, 2001 4530 75 25 3397.5 1132.5

Following the splitting of an injector well into multiple zonal levelinjectors, a capacitance resistance model is generated in which eachzonal level injector is treated as a single injector, thereforeinterwell connectivities are obtained at the zonal level for at leastone injector and the producers that it supports. In the running examplewith one injector (I1) and two zones (Z1 and Z2) and two producers (P1and P2), interwell connectivities are obtained at the zonal level (fij)between injector I1 at zone Z1 and zone Z2 with the two producers P2 andP3 as shown in table below:

CRM Connectivity at Zonal level- fij I1Z1-P2 I1Z2-P2 I1Z1-P3 I1Z2-P3 0.20.4 0.8 0.6

The injected fluid can have a preferred flow path in different zones.The preferential flow path in the running example is shown in Tableabove. It shows that the injected fluid has a preferred flow pathbetween Injector I1 and Producer P3 in zone Z2.

Secondary Production Application: Continuous production profile in caseof if only secondary recovery is contributing in production is obtainedfrom injection rates and zonal level connectivities by summing upcontribution of zonal injectors and their connectivities to at least oneof the producers.

${{{At}\mspace{14mu}{Zone}\mspace{14mu} 1\mspace{14mu}{for}\mspace{14mu}{Producer}\mspace{14mu} j\text{:}\mspace{14mu}{q_{j}^{Z\; 1}(t)}} = {\sum\limits_{i = 1}^{N_{inj}}\left\lbrack {f_{ij}^{Z\; 1}{I_{i}^{({Z\; 1})}(t)}} \right\rbrack}}\mspace{14mu}$(for  j = 1, 2, … , N_(pro))

Considering the zonal level connectivities and injection rates, of theexample above, CRM estimates of continuous production rate at the zonallevel for contributing zones of producer P2 and P3 are obtained andshown in table below,

CRM Estimates of Secondary flux at zone level (qij = Injection rate byzone multiplied by connectivity @ zone 1 and 2) Date I1Z1-P2 I1Z2-P2I1Z1-P3 I1Z2-P3 Jan. 1, 2000 368 1060 1473 1589 Feb. 1, 2000 98 240 392360 Mar. 1, 2000 101 810 405 1214 Apr. 1, 2000 542 94 2171 142 May 1,2000 209 402 836 603 Jun. 1, 2000 735 145 2941 218 Jul. 1, 2000 192 819771 1228 Aug. 1, 2000 14 1427 58 2140 Sep. 1, 2000 396 391 1587 586 Oct.1, 2000 471 349 1886 523 Nov. 1, 2000 468 831 1874 1246 Dec. 1, 2000 201513 806 769 Jan. 1, 2001 32 1567 131 2350 Feb. 1, 2001 546 859 2186 1288Mar. 1, 2001 150 611 602 917 Apr. 1, 2001 241 309 966 463 May 1, 2001 561771 226 2656 Jun. 1, 2001 679 453 2718 680

CRM estimates of production rates for contributing zone is normalized toprovide production split of each zone as a continues value during theproduction of the producers, as shown in table below, these estimate arecross checked with sparse production profile data which also isconsidered in calibration of the CRM zonal level analysis.

check Check for P2, for P3, Date Z1-P2 Z2-P2 sum = 1 Z1-P3 Z2-P3 sum = 1Jan. 1, 2000 0.26 0.74 1.00 0.48 0.52 1.00 Feb. 1, 2000 0.29 0.71 1.000.52 0.48 1.00 Mar. 1, 2000 0.11 0.89 1.00 0.25 0.75 1.00 Apr. 1, 20000.85 0.15 1.00 0.94 0.06 1.00 May 1, 2000 0.34 0.66 1.00 0.58 0.42 1.00Jun. 1, 2000 0.83 0.17 1.00 0.93 0.07 1.00 Jul. 1, 2000 0.19 0.81 1.000.39 0.61 1.00 Aug. 1, 2000 0.01 0.99 1.00 0.03 0.97 1.00 Sep. 1, 20000.50 0.50 1.00 0.73 0.27 1.00 Oct. 1, 2000 0.57 0.43 1.00 0.78 0.22 1.00Nov. 1, 2000 0.36 0.64 1.00 0.60 0.40 1.00 Dec. 1, 2000 0.28 0.72 1.000.51 0.49 1.00 Jan. 1, 2001 0.02 0.98 1.00 0.05 0.95 1.00 Feb. 1, 20010.39 0.61 1.00 0.63 0.37 1.00 Mar. 1, 2001 0.20 0.80 1.00 0.40 0.60 1.00Apr. 1, 2001 0.44 0.56 1.00 0.68 0.32 1.00 May 1, 2001 0.03 0.97 1.000.08 0.92 1.00 Jun. 1, 2001 0.60 0.40 1.00 0.80 0.20 1.00

Primary and Secondary Production Application: Continuous productionprofile from primary and secondary production is obtained by using theprimary and secondary recovery portion of CRM estimate once the CRMzonal is performed. The Primary portion of production profile isestimated from CRM zonal level primary portion which is an exponentialdecline. The secondary portion of production rates at zonal level areobtained from summing the multiplication of injection rates andconnectivities at zonal level from all injectors contributing in theproduction of a given producer.

Primary  Portion  of  CRM:  ${{q_{j}\left( t_{0} \right)}e^{- {(\frac{t_{n} - t_{0}}{\tau_{{Primary},j}})}}} - {J_{j}\tau_{{Primary},j}\frac{\Delta\; p_{{wf},j}^{(k)}}{\Delta\; t_{k}}\left( {1 - e^{\frac{{- \Delta}\; t_{k}}{\tau_{{Ptimary},j}}}} \right)}$${Secondary}\mspace{14mu}{Portion}\mspace{14mu}{of}\mspace{11mu}{CRM}\text{:}\mspace{14mu}\left( {1 - e^{\frac{{- \Delta}\; t_{k}}{\tau_{{Secondary},j}}}} \right)\left( {\sum\limits_{i = 1}^{N_{inj}}\left\lbrack {f_{ij}I_{i}^{(k)}} \right\rbrack} \right)$(for  j = 1, 2, … , N_(pro))

In the running example table below shows historical production, CRMestimate and its primary and secondary portions estimated from equationabove,

Production Rate History Primary + Secondary Primary Pro- Pro- SecondaryPortion Portion ducer ducer CRM CRM CRM CRM CRM CRM Date P2 P3 P2 P3 P2P3 P2 P3 Jan. 1, 2000 2168 3717 2303 4032 1428 3062 876 969 Feb. 1, 2000887 1288 950 1349 338 752 612 597 Mar. 1, 2000 1317 2058 1414 2144 9111619 503 525 Apr. 1, 2000 938 2672 1020 2732 636 2313 383 419 May 1,2000 834 1733 890 1813 611 1439 279 373 Jun. 1, 2000 1046 3400 1092 3501880 3159 212 342 Jul. 1, 2000 1133 2196 1184 2406 1011 1999 173 408 Aug.1, 2000 1532 2360 1658 2462 1441 2199 218 264 Sep. 1, 2000 854 2305 8842382 787 2173 97 210 Oct. 1, 2000 870 2518 893 2631 820 2410 73 221 Nov.1, 2000 1336 3209 1376 3286 1299 3121 77 165 Dec. 1, 2000 741 1648 8081696 714 1576 95 121 Jan. 1, 2001 1619 2540 1741 2645 1599 2481 142 164Feb. 1, 2001 1420 3523 1516 3831 1405 3475 111 357 Mar. 1, 2001 772 1558800 1587 761 1518 39 68 Apr. 1, 2001 558 1462 566 1521 550 1430 16 92May 1, 2001 1833 2909 1966 2972 1827 2883 139 90 Jun. 1, 2001 1137 34191198 3492 1132 3398 66 95

Similar to the case of no primary recovery zonal contribution from eachof producing zones for secondary production is estimated using CRM zonallevel interwell connectivities as shown before while the primaryrecovery contribution is evaluated by using at least one measurement ofproduction profile data for each producer to divide the primaryproduction portion of to its contributing zones, in the example of theproducer P2 CRM estimates a total production rate of 950 bbl/day(highlighted in table above), and profile is obtained on February 2000indicating 332 (234+98) and 618 (378+240) bbl/day production for Zone Z1and Z2 accordingly; and for producer P3 CRM estimates a total productionrate of 1813 bbl/day (highlighted in table above), a profile is obtainedon May 2000 indicating the production rate of 906 (Secondary portion70+primary portion 836) and 907 (304+603) bbl/day production for Zone Z1and Z2 accordingly; therefore, the Primary production portion of a wellis divided accordingly to make up for the remaining production of eachzone in February 2000 and May 2000 for Producer P2 and P3:

Secondary Portions Primary Portions Date I1Z1-P2 I1Z2-P2 I1Z1-P3 I1Z2-P3Z1-P2 Z2-P2 Z1-P3 Z2-P3 Jan. 1, 2000 368 1060 1473 1589 335 540 181 788Feb. 1, 2000 98 240 392 360 234 378 112 485 Mar. 1, 2000 101 810 4051214 193 310 98 427 Apr. 1, 2000 542 94 2171 142 147 237 78 341 May 1,2000 209 402 836 603 107 172 70 304 Jun. 1, 2000 735 145 2941 218 81 13164 278 Jul. 1, 2000 192 819 771 1228 66 107 76 331 Aug. 1, 2000 14 142758 2140 83 134 49 214 Sep. 1, 2000 396 391 1587 586 37 60 39 171 Oct. 1,2000 471 349 1886 523 28 45 41 180 Nov. 1, 2000 468 831 1874 1246 29 4731 134 Dec. 1, 2000 201 513 806 769 36 58 23 98 Jan. 1, 2001 32 1567 1312350 54 87 31 134 Feb. 1, 2001 546 859 2186 1288 42 68 67 290 Mar. 1,2001 150 611 602 917 15 24 13 56 Apr. 1, 2001 241 309 966 463 6 10 17 75May 1, 2001 56 1771 226 2656 53 86 17 73 Jun. 1, 2001 679 453 2718 68025 41 18 77

In the running example, CRM estimates of production rates for eachcontributing zone is then normalized to provide production split of eachzone as a continuous value during the production of the producers forboth primary and secondary contributions; as shown in table below, theseestimate are cross checked with sparse production profile data whichalso is considered in calibration of the CRM zonal level analysis.

CRM PLT Estimates (Primary + Secondary) Z1- Z2- Z1- Time P2 P2 Check P3Z2-P3 Check Jan. 1, 2000 0.31 0.69 Measured P2 0.41 0.59 Measured Feb.1, 2000 0.35 0.65 0.37 0.63 P3 Mar. 1, 2000 0.21 0.79 0.23 0.77 Apr. 1,2000 0.68 0.32 0.82 0.18 May 1, 2000 0.35 0.65 0.50 0.50 Jun. 1, 20000.75 0.25 0.86 0.14 Jul. 1, 2000 0.22 0.78 0.35 0.65 Aug. 1, 2000 0.060.94 0.04 0.96 Sep. 1, 2000 0.49 0.51 0.68 0.32 Oct. 1, 2000 0.56 0.440.73 0.27 Nov. 1, 2000 0.36 0.64 0.58 0.42 Dec. 1, 2000 0.29 0.71 0.490.51 Jan. 1, 2001 0.05 0.95 0.06 0.94 Feb. 1, 2001 0.39 0.61 0.59 0.41Mar. 1, 2001 0.21 0.79 0.39 0.61 Apr. 1, 2001 0.44 0.56 0.65 0.35 May 1,2001 0.06 0.94 0.08 0.92 Jun. 1, 2001 0.59 0.41 0.78 0.22

Those of ordinary skill in the art will appreciate that modificationsmay be made to the illustrated embodiments, such as the illustrated CRMzonal embodiments (see also FIGS. 34, 35A-35E, 36A-36H, 37A-37I). Forexample, VOIF may be determined at a well level or at a zonal level.Furthermore, sensitivity analysis may be used. Also, for example, (i)sensitivity analysis may be performed to determine the allowed wellconnections for CRM, (ii) CRM may be run with sensitivity analysis,(iii) VOIF may be determined for an injection well (or zone) at thesteady state with or without sensitivity analysis, and/or (iv) VOIF maybe determined for an injection well (or zone) as transient with orwithout sensitivity analysis. CRM zonal may also be used to determineconformance candidates at zonal level and/or to make recommendations asdiscussed herein. VOIF may also be used to recommend a conformancecandidate (e.g., if f*ij or fij is more than a threshold such as 0.4,and if VOIF is less than a threshold such as 0.1, and if injection rateis more than average, then the method 3300 may recommend thecorresponding well and/or zone as a conformance candidate). Criteria mayalso be used to recommend a stimulation candidate. Thus, the method 3400may generate a recommendation. Also, in some embodiments, PLT can be asubstitute for oil-cut in determining VOIF.

Furthermore, CRM zonal may be used to determining if there is crossflowbetween at least two zones. For example, allocation factors may bereceived or determined from ILT/PLT, K H Q, or other zonal splits (e.g.,from zonal application 218). Next, CRM may be run with the assumptionthat each zone is treated like an injector. Next, the method may includecalculating CRM based zonal splits for producers by zone. Next, themethod may compare the CRM based zonal splits with those received todetermine if there is crossflow.

Those of ordinary skill in the art will appreciate that CRM zonal may beused to identify the value of injected fluid for each zone of ahydrocarbon reservoir (as discussed in the VOIF section herein), togenerate PLTs, to identify conformance control issues (as discussed inthe conformance control section herein), etc.

While many details have been set forth for purpose of illustration, itwill be apparent to those skilled in the art that the disclosure issusceptible to alteration and that certain other details describedherein can vary considerably without departing from the basic principlesof the invention. In addition, it should be appreciated that structuralfeatures or method steps shown or described in any one embodiment hereincan be used in other embodiments as well.

For the avoidance of doubt, the present application includes thesubject-matter defined in the following numbered paragraphs:

Allowed Connections

A1. A computer implemented method of analyzing a flood operation for ahydrocarbon reservoir, the method comprising: establishing a dataset ofallowed well connections for at least one production well and at leastone injection well of the hydrocarbon reservoir; iteratively modifyingthe dataset of allowed well connections based on a distance category, astatic features category, a dynamic features category, or anycombination thereof, wherein the static features category uses staticdata of the hydrocarbon reservoir and the dynamic features category usesdynamic data of the hydrocarbon reservoir; and after iterativelymodifying the dataset of allowed well connections, running capacitanceresistance modeling using the modified dataset of allowed wellconnections as an input.

A2. The method of paragraph A1, further comprising using at least oneinterwell connectivity generated by running capacitance resistancemodeling to modify the dataset of allowed well connections.

A3. The method of paragraph A1, wherein the hydrocarbon reservoirincludes a plurality of zones, and wherein each zone is treated as aninjection well.

A4. The method of paragraph A1, wherein iteratively modifying thedataset of allowed well connections includes using a priority betweenthe categories.

A5. The method of paragraph A1, wherein iteratively modifying thedataset of allowed well connections based on the distance categoryincludes using a priority within the distance category, a sensitivityanalysis for the distance category, or both.

A6. The method of paragraph A1, wherein iteratively modifying thedataset of allowed well connections based on the static featurescategory includes using a priority within the static features category,a sensitivity analysis for the static features category, or both.

A7. The method of paragraph A1, wherein iteratively modifying thedataset of allowed well connections based on the dynamic featurescategory includes using a priority within the dynamic features category,a sensitivity analysis for the dynamic features category, or both.

A8. The method of paragraph A1, wherein the static data includesgeological data, seismic data, three dimensional (3D) seismic data,faults, fractures, geobodies, geological boundaries, a processed versionof any of these, or any combination thereof.

A9. The method of paragraph A1, wherein the dynamic data includes wellproduction data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, capacitance resistancemodeling output data, polygon data, a processed version of any of these,or any combination thereof.

A10. The method of paragraph A1, further comprising updating at leastone application based on output from the capacitance resistancemodeling.

A11. A computer implemented method of analyzing a flood operation for ahydrocarbon reservoir, the method comprising: determining a dataset ofallowed well connections for at least one production well and the atleast one injection well of the hydrocarbon reservoir based on adistance category; determining a dataset of allowed well connections forthe at least one production well and the at least one injection well ofthe hydrocarbon reservoir based on a static features category, whereinthe static features category uses static data of the hydrocarbonreservoir; determining a dataset of allowed well connections for the atleast one production well and the at least one injection well of thehydrocarbon reservoir based on a dynamic features category, wherein thedynamic features category uses dynamic data of the hydrocarbonreservoir; comparing the determined allowed well connections from thevarious datasets; combining the determined allowed well connections fromthe various datasets based on the comparison and a priority into acombined dataset of allowed well connections; and running capacitanceresistance modeling using the combined dataset of allowed wellconnections as an input.

A12. The method of paragraph A11, further comprising using at least oneinterwell connectivity generated by running capacitance resistancemodeling to modify the combined dataset of allowed well connections.

A13. The method of paragraph A11, wherein the hydrocarbon reservoirincludes a plurality of zones, and wherein each zone is treated as aninjection well.

A14. The method of paragraph A11, wherein determining the dataset ofallowed well connections based on the distance category includes using apriority within the distance category, a sensitivity analysis for thedistance category, or both.

A15. The method of paragraph A11, wherein determining the dataset ofallowed well connections based on the static features category includesusing a priority within the static features category, a sensitivityanalysis for the static features category, or both.

A16. The method of paragraph A11, wherein determining the dataset ofallowed well connections based on the dynamic features category includesusing a priority within the dynamic features category, a sensitivityanalysis for the dynamic features category, or both.

A17. The method of paragraph A11, wherein the static data includesgeological data, seismic data, three dimensional (3D) seismic data,faults, fractures, geobodies, geological boundaries, a processed versionof any of these, or any combination thereof.

A18. The method of paragraph A11, wherein the dynamic data includes wellproduction data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, capacitance resistancemodeling output data, polygon data, a processed version of any of these,or any combination thereof.

A19. The method of paragraph A1, further comprising updating at leastone application based on output from the capacitance resistancemodeling.

A20. A computing system for analyzing a flood operation for ahydrocarbon reservoir, the system comprising: at least one processor;and at least one memory containing computer executable instructions,that when executed by the at least one processor, cause the computingsystem to: establish a dataset of allowed well connections for at leastone production well and at least one injection well of the hydrocarbonreservoir; iteratively modify the dataset of allowed well connectionsbased on a distance category, a static features category, a dynamicfeatures category, or any combination thereof, wherein the staticfeatures category uses static data of the hydrocarbon reservoir and thedynamic features category uses dynamic data of the hydrocarbonreservoir; and after iteratively modifying the dataset of allowed wellconnections, run capacitance resistance modeling using the modifieddataset of allowed well connections as an input.

Comparing Different Flood Operations/PF

B1. A computer implemented method of analyzing at least a first floodoperation and a second flood operation on a hydrocarbon reservoir havingat least one production well and at least one injection well, the methodcomprising: for each of the first flood operation and the second floodoperation: receiving production data for the at least one productionwell and injection data for the at least one injection well for theflood operation; running capacitance resistance modeling using thereceived production data and the received injection data for the floodoperation to generate a response time and an interwell connectivity perinjection well and production well pair for the flood operation; usingthe generated response times, the generated interwell connectivities,the received production data, the received injection data, or anycombination thereof to generate a proxy of pore volume swept perinjection well and production well pair for the flood operation; andaggregating the generated proxies of pore volume swept per injectionwell and production well pair for the flood operation to generate anestimate of pore volume swept at a well level, at a reservoir level, orboth for the flood operation; and comparing the generated estimate ofpore volume swept for the first flood operation and the generatedestimate of pore volume swept for the second flood operation todetermine a change in sweep efficiency at the well level, at thereservoir level, or both.

B2. The method of paragraph B1, further comprising, for each of thefirst flood operation and the second flood operation, determiningheterogeneity at the well level, the reservoir level, or both.

B3. The method of paragraph B2, further comprising comparing thedetermined heterogeneity of the first flood operation and the determinedheterogeneity of the second flood operation to determine a change inheterogeneity at the well level, at the reservoir level, or both.

B4. The method of paragraph B3, further comprising comparing thedetermined change in heterogeneity and the determined change in sweepefficiency to verify accuracy of the determined change in sweepefficiency.

B5. The method of paragraph B1, wherein the hydrocarbon reservoirincludes a plurality of zones, and wherein each zone is treated as aninjection well.

B6. The method of paragraph B1, wherein the production data includesproduction rate and flowing pressure data as a function of time, andwherein the injection data includes injection rate and flowing pressuredata as a function of time.

B7. The method of paragraph B1, wherein the first flood operation, thesecond flood operation, or both is a polymer flood operation, furthercomprising accounting for rheology of the polymer used in the polymerflood operation in the running of the capacitance resistance modeling.

B8. The method of paragraph B7, wherein accounting for the rheology inrunning capacitance resistance modeling includes separating eachinjection well and production well pair of the polymer flood operationinto at least three tanks, wherein the three tanks include a nearinjection well tank, a near production well tank, and a middle tankbetween the near injection well tank and the near production well tank.

B9. The method of paragraph B8, wherein the production data includesproduction rate and flowing pressure data as a function of time, andwherein the injection data includes injection rate and flowing pressuredata as a function of time, and wherein accounting for the rheology inrunning capacitance resistance modeling includes using (i) materialbalance equations for each of the tanks, (ii) the injection andproduction rates, (iii) the injection and production flowing pressuredata, and (iv) a polymer rheology.

B10. The method of paragraph B8, wherein accounting for the rheology inrunning capacitance resistance modeling includes using a capacitanceresistance modeling polymer formulation:

${{{\overset{\_}{\tau}}_{j}\tau_{j}\frac{\partial\;}{\partial t}\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + {{\overset{\_}{\tau}}_{j}\frac{\partial\;}{\partial t}\left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} + {\left( {{{\overset{\_}{\tau}}_{j}\frac{{\overset{\_}{J}}_{j}}{J_{j}}} + \tau_{j}} \right)\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + \left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} = {\sum\limits_{i = 1}^{Nt}{f_{i,j}\left( {{i_{i,p}(t)} + {\tau_{i}\left( {\frac{\partial{i_{i,p}^{n}(t)}}{\partial t} - {J_{i}\frac{\partial P_{{wf},i}}{\partial t}}} \right)}} \right)}}$wherein τ_(i) is a time constant for the near injection well tankassociated with injection well i, τ_(j) is a time constant for the nearproduction well tank associated with production well j, τ _(j) is a timeconstant for the middle tank, J_(i) is an injectivity index forinjection well i, J_(j) is a productivity index for production well j, J_(j) is a productivity index for the middle tank, P_(wf,j) is a wellflowing pressure for production well j, P_(wf,i) is a well flowingpressure for injection well i, i_(i,p)(t) is a flow rate of the polymerin injection well i, q_(p,j)(t) is a polymer flow rate in productionwell j, q_(o,j)(t) is an oil flow rate in production well j and ndefines the polymer rheology.

B11. A computing system for analyzing at least a first flood operationand a second flood operation on a hydrocarbon reservoir having at leastone production well and at least one injection well, the systemcomprising: at least one processor; and at least one memory containingcomputer executable instructions, that when executed by the at least oneprocessor, cause the computing system to perform a method comprising:for each of the first flood operation and the second flood operation:receiving production data for the at least one production well andinjection data for the at least one injection well for the floodoperation; running capacitance resistance modeling using the receivedproduction data and the received injection data for the flood operationto generate a response time and an interwell connectivity per injectionwell and production well pair for the flood operation; using thegenerated response times, the generated interwell connectivities, thereceived production data, the received injection data, or anycombination thereof to generate a proxy of pore volume swept perinjection well and production well pair for the flood operation; andaggregating the generated proxies of pore volume swept per injectionwell and production well pair for the flood operation to generate anestimate of pore volume swept at a well level, at a reservoir level, orboth for the flood operation; and comparing the generated estimate ofpore volume swept for the first flood operation and the generatedestimate of pore volume swept for the second flood operation todetermine a change in sweep efficiency at the well level, at thereservoir level, or both.

B12. The computing system of paragraph B11, wherein the computerexecutable instructions are further configured to, for each of the firstflood operation and the second flood operation, determine heterogeneityat the well level, the reservoir level, or both.

B13. The computing system of paragraph B12, wherein the computerexecutable instructions are further configured to compare the determinedheterogeneity of the first flood operation and the determinedheterogeneity of the second flood operation to determine a change inheterogeneity at the well level, at the reservoir level, or both.

B14. The computing system of paragraph B13, wherein the computerexecutable instructions are further configured to compare the determinedchange in heterogeneity and the determined change in sweep efficiency toverify accuracy of the determined change in sweep efficiency.

B15. The computing system of paragraph B11, wherein the hydrocarbonreservoir includes a plurality of zones, and wherein each zone istreated as an injection well.

B16. The computing system of paragraph B11, wherein the first floodoperation, the second flood operation, or both is a polymer floodoperation, and wherein the computer executable instructions are furtherconfigured to account for rheology of the polymer used in the polymerflood operation in the running of the capacitance resistance modeling.

B17. A computer implemented method of analyzing a polymer floodoperation on a hydrocarbon reservoir having at least one injection welland at least one production well, the method further comprising:receiving production data for the at least one production well andinjection data for the at least one injection well for the floodoperation; and running capacitance resistance modeling using thereceived production data and the received injection data for the polymerflood operation to generate a response time and an interwellconnectivity per injection well and production well pair for the polymerflood operation, wherein running capacitance resistance modelingincludes accounting for rheology of the polymer used in the polymerflood operation in the running of the capacitance resistance modeling.

B18. The method of paragraph B17, wherein accounting for the rheology inrunning capacitance resistance modeling includes separating eachinjection well and production well pair of the polymer flood operationinto at least three tanks, wherein the three tanks include a nearinjection well tank, a near production well tank, and a middle tankbetween the near injection well tank and the near production well tank.

B19. The method of paragraph B18, wherein the production data includesproduction rate and flowing pressure data as a function of time, andwherein the injection data includes injection rate and flowing pressuredata as a function of time, and wherein accounting for the rheology inrunning capacitance resistance modeling includes using (i) materialbalance equations for each of the tanks, (ii) the injection andproduction rates, (iii) the injection and production flowing pressuredata, and (iv) a polymer rheology.

B20. The method of paragraph B18, wherein accounting for the rheology inrunning capacitance resistance modeling includes using a capacitanceresistance modeling polymer formulation:

${{{\overset{\_}{\tau}}_{j}\tau_{j}\frac{\partial\;}{\partial t}\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + {{\overset{\_}{\tau}}_{j}\frac{\partial\;}{\partial t}\left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} + {\left( {{{\overset{\_}{\tau}}_{j}\frac{{\overset{\_}{J}}_{j}}{J_{j}}} + \tau_{j}} \right)\left( {\frac{\partial\left( {{q_{p,j}^{n}(t)} + {q_{o,j}(t)}} \right)}{\partial t} + {J_{j}\frac{\partial P_{{wf},j}}{\partial t}}} \right)} + \left( {{q_{p,j}(t)} + {q_{o,j}(t)}} \right)} = {\sum\limits_{i = 1}^{Nt}{f_{i,j}\left( {{i_{i,p}(t)} + {\tau_{i}\left( {\frac{\partial{i_{i,p}^{n}(t)}}{\partial t} - {J_{i}\frac{\partial P_{{wf},i}}{\partial t}}} \right)}} \right)}}$wherein τ_(i) is a time constant for the near injection well tankassociated with injection well i, τ_(j) is a time constant for the nearproduction well tank associated with production well j, τ _(j) is a timeconstant for the middle tank, J_(i) is an injectivity index forinjection well i, J_(j) is a productivity index for production well j, J_(j) is a productivity index for the middle tank, P_(wf,j) is a wellflowing pressure for production well j, P_(wf,i) is a well flowingpressure for injection well i, i_(i,p)(t) is a flow rate of the polymerin injection well i, q_(p,j)(t) is a polymer flow rate in productionwell j, q_(o,j)(t) is an oil flow rate in production well j and ndefines the polymer rheology.

Pattern Management

C1. A computer implemented method of using producer centered polygons toidentify at least one infill drilling location in a hydrocarbonreservoir having a plurality of producers and at least one injector, themethod comprising: load well locations, reservoir boundary, andinjection rate and production rate histories; creating producer-centeredpolygons based on the producer locations and the reservoir boundary;calculating an area (A_(i)) of any given producer by each polygonassociated with each producer based on geometry or the geologicalboundary of the polygon; for each producer, calculating cumulative oilproduction (Qi) and a ratio between Qi and Ai; ranking the producersfrom smallest to largest Q/A ratio; and locating infill drilling placesin the polygons with high-ranking producers.

C2. The method of paragraph C1, wherein the producer centered polygonsare producer centered Voronoi polygons.

C3. The method of paragraph C1, wherein the area is a drainage area, apore volume, a proxy for OOIP, or any combination thereof.

C4. A computer implemented method of using polygons to choose between afirst infill candidate in a first hydrocarbon reservoir and a secondinfill candidate in a different second hydrocarbon reservoir, whereineach of the hydrocarbon reservoirs has a plurality of producers and atleast one injector, the method comprising: for the first hydrocarbonreservoir: loading well locations, reservoir boundary, and injectionrate and production rate histories; creating producer-centered polygonsbased on the producer locations and the reservoir boundary; calculatingan area covered (Ai) by each polygon associated with each producer basedon geometry of the polygon; for each producer, calculating cumulativeoil production (Qi) and rank producers from largest Q to smallest Q; foreach producer, calculating cumulative oil production (Qi) and a ratiobetween Qi and Ai; ranking the producers from smallest to largest Q/Aratio; calculating a norm area and norm cumulative oil production in thefirst reservoir; calculating an index of the uneven sweep (IUS) of thefirst reservoir.

C5. The method of paragraph C4, further comprising repeat steps ofparagraph C1 for the second reservoir.

C6. The method of paragraph C5, further comprising selecting thereservoir for infill drilling opportunity with the largest IUS.

C7. The method of paragraph C4, wherein the producer centered polygonsare producer centered Voronoi polygons.

C8. The method of paragraph C4, wherein the area is a drainage area, apore volume, a proxy for OOIP, or any combination thereof.

C9. The method of paragraph C4, wherein the norm area is pore volume.

C10. A computer implemented method of using streamgrids for patternrealignment for a hydrocarbon reservoir having at least one producer andat least one injector, the method comprising: loading well locations andinjection rate and production rate histories for all wells; creatingstreamgrid based on the well locations; calculating allocation factorsfor injector and producers based an allocation method; calculatingallocated water injection and water production within each streamgridbetween connected injector-producer pair) calculating water cyclingbetween connected injector-producer pair for each streamgrid; defining athreshold for the water cycling based on a distribution; and identifyingat least one streamgrid that has water cycling above the threshold forconverting the producer in the identified streamgrid for patternrealignment

C11. The method of paragraph C10, wherein the allocation method is aninjection angle, a producer angle, or any combination thereof.

C12. The method of paragraph C10, wherein the allocated water injectionand water production within each streamgrid are calculated betweenconnected injector-producer pair.

C13. A computer implemented method of using streamgrid allocationfactors to initiate capacitance resistance modeling interwellconnectivity, the method comprising: loading well locations andinjection rate and production rate histories for all wells of ahydrocarbon reservoir having at least one producer and at least oneinjector; creating streamgrid based on well locations; calculatingallocation factors for injector based on and allocation method;exporting the allocation factors as initial values of interwellconnectivity between well pairs in the capacitance resistance modeling.

C14. The method of paragraph C13, wherein the allocation method is aninjection angle, number of producers, injector Area, or any combinationthereof.

C15. A computer implemented method of estimating maximal areal sweep byzone and by reservoir with populated streamgrids, the method comprising:for any given zone of a reservoir: getting the reservoir boundary inthat zone and calculating its total area (St); getting contact point ofall wells that penetrate and are perforated in that zone; creatingstreamgrid with well-zone contact locations; calculating total area (S)covered by populated streamgrids; and estimating maximal areal sweepefficiency in that zone, wherein the maximal areal sweep efficiencyequals S/St.

C16. The method of paragraph C15, further comprising repeating steps ofparagraph C1 for all remaining zones in the reservoir to get the S andSt for each zone.

C17. The method of paragraph C16, further comprising calculating a ratiobetween summation of S from all zones and summation of St from all zonesto get a maximum areal sweep efficiency for the reservoir.

Conformance Control

D1. A computer implemented method of identifying conformance candidates,the method comprising: for a first entity: solving an injection entityindex to generate an injection entity index value, wherein the injectionentity index includes injection efficiency, value of injected fluid, andpore volume injected for a period of time; solving a production entityindex to generate a production entity index value, wherein theproduction entity index includes estimate of remaining movable oil inplace; evaluating an operation entity index that represents operationstatus to generate an operation entity index value; and combining theinjection entity index value, the production entity index value, and theoperation entity index value to generate a conformance problem indexvalue for the first entity.

D2. The method of paragraph D1, further comprising comparing thegenerated conformance problem index value for the first and a thresholdto determine if the first entity is a conformance candidate.

D3. The method of paragraph D1, further comprising generating aconformance problem index value for at least one other entity.

D4. The method of paragraph D3, further comprising ranking the firstentity and the at least one other entity based on the generatedconformance problem index values.

D5. The method of paragraph D4, wherein a higher generated conformanceproblem index value indicates a higher likelihood of a conformanceproblem.

D6. The method of paragraph D4, wherein the first entity has the highestgenerated conformance problem index value, and wherein the first entityis at a field level, further comprising generating a conformance problemindex value at a reservoir level, well level, a zone level, or anycombination thereof.

D7. The method of paragraph D4, wherein the first entity has the highestgenerated conformance problem index value, and wherein the first entityis at a reservoir level, further comprising generating a conformanceproblem index value at a well level, a zone level, or any combinationthereof.

D8. The method of paragraph D4, wherein the first entity has the highestgenerated conformance problem index value, and wherein the first entityis at a well level, further comprising generating a conformance problemindex value at a zone level.

D9. The method of paragraph D1, wherein the first entity is at a fieldlevel, a reservoir level, a well level, a zone level, or any combinationthereof.

D10. The method of paragraph D4, wherein the first entity has thehighest generated conformance problem index value, and wherein the firstentity includes a plurality of zones, further comprising: receiving datafor each zone of the plurality of zones, the received data to be used todetermine a residence time distribution for each zone; determining theresidence time distribution for each zone of the plurality of zonesusing the received data; identifying at least one zone of the pluralityof zones to be treated with a conformance agent by comparing thedetermined residence time distributions of the zones and a residencetime distribution threshold; and recommending a first conformancecontrol treatment at breakthrough time of the slowest identified zonefor the at least one identified zone.

D11. The method of paragraph D10, further comprising recommending a slugsize for the first conformance control treatment, wherein the slug sizeis determined by calculating injected volume for a period of time whichis not more than 50% of a breakthrough time for the fastest identifiedzone.

D12. The method of paragraph D10, further comprising recommending aconcentration of the conformance agent for the first conformance controltreatment, wherein the concentration of the conformance agent is basedon a resistance factor of the conformance agent and a rheology of theconformance agent.

D13. The method of paragraph D10, further comprising: receiving updateddata for each zone of the plurality of zones after the first conformancecontrol treatment; determining an updated residence time distributionfor each zone of the plurality of zone after the first conformancecontrol treatment; identifying at least one zone of the plurality ofzones after the first conformance control treatment to be treated with aconformance agent in a second conformance control treatment based on acomparison of the updated residence time distribution of each zone andthe residence time distribution threshold; and recommending a secondconformance control treatment at an updated breakthrough time of theslowest identified zone after the first conformance control treatmentfor the at least one identified zone after the first conformance controltreatment.

D14. A computer implemented method of determining a conformance controltreatment for a well of a hydrocarbon reservoir, wherein the well is influidic communication with a plurality of zones of the hydrocarbonreservoir, the method comprising: receiving data for each zone of theplurality of zones, the received data to be used to determine aresidence time distribution for each zone; determining the residencetime distribution for each zone of the plurality of zones using thereceived data; identifying at least one zone of the plurality of zonesto be treated with a conformance agent by comparing the determinedresidence time distributions of the zones and a residence timedistribution threshold; and recommending a first conformance controltreatment at breakthrough time of the slowest identified zone for the atleast one identified zone.

D15. The method of paragraph D14, further comprising recommending a slugsize for the first conformance control treatment, wherein the slug sizeis determined by calculating injected volume for a period of time whichis not more than 50% of a breakthrough time for the fastest identifiedzone.

D16. The method of paragraph D14, further comprising recommending aconcentration of the conformance agent for the first conformance controltreatment, wherein the concentration of the conformance agent is basedon a resistance factor of the conformance agent and a rheology of theconformance agent.

D17. The method of paragraph D16, wherein the resistance factor of theconformance agent is sufficient to reduce original velocity of thefastest identified zone by a factor of about 10.

D18. The method of paragraph D14, further comprising: receiving updateddata for each zone of the plurality of zones after the first conformancecontrol treatment; determining an updated residence time distributionfor each zone of the plurality of zone after the first conformancecontrol treatment; identifying at least one zone of the plurality ofzones after the first conformance control treatment to be treated with aconformance agent in a second conformance control treatment based on acomparison of the updated residence time distribution of each zone andthe residence time distribution threshold; and recommending a secondconformance control treatment at an updated breakthrough time of theslowest identified zone after the first conformance control treatmentfor the at least one identified zone after the first conformance controltreatment.

D19. The method of paragraph D18, further comprising recommending a slugsize for the second conformance control treatment, wherein the slug sizefor the second conformance control treatment is determined bycalculating injected volume for a period of time which is not more than50% of an updated breakthrough time for the fastest identified zone.

D20. The method of paragraph D18, further comprising recommending aconcentration of the conformance agent for the second conformancecontrol treatment, wherein the concentration of the conformance agentfor the second conformance control treatment is based on a resistancefactor of the conformance agent for the second conformance controltreatment and a rheology of the conformance agent for the secondconformance control treatment.

D21. The method of paragraph D20, wherein the resistance factor of theconformance agent of the second conformance control treatment issufficient to reduce updated velocity of the fastest identified zoneafter the first conformance control treatment by a factor of about 10.

D22. A computing system for identifying conformance candidates, thesystem comprising: at least one processor; and at least one memorycontaining computer executable instructions, that when executed by theat least one processor, cause the computing system to perform a methodcomprising: for a first entity: solving an injection entity index togenerate an injection entity index value, wherein the injection entityindex includes injection efficiency, value of injected fluid, and porevolume injected for a period of time, solving an production entity indexto generate an production entity index value, wherein the productionentity index includes estimate of remaining movable oil in place;evaluating an operation entity index that represents operation status togenerate an operation entity index value; and combining the injectionentity index value, production entity index value, and the operationentity index value to generate a conformance problem index value for thefirst entity.

Heavy Oil Flood Operation

E1. A computer implemented method of analyzing a flood operation on ahydrocarbon reservoir having heavy oil, the method comprising: for afirst injection well of the hydrocarbon reservoir: receiving injectionrate data and production rate data for production wells communicatingwith the first injection well for a period of time; establishing aplurality of time windows based on breakthrough time of each productionwell that has broken through until the current date, wherein theproduction rate data is indicative of breakthrough; running capacitanceresistance modeling for each established time window to generateinterwell connectivities for each well pair; solving a continuousfunction for each well pair using the capacitance resistance modelinggenerated interwell connectivity of the well pair over time to estimateinterwell connectivity for each well pair in the future; using theproduction rate data to solve an oil-cut model for each production wellthat has broken through to estimate oil-cut value for each productionwell in the future; and determining a value of injected fluid for thefirst injection well as a function of time in the future using thecontinuous function estimated interwell connectivity and the estimatedoil-cut value.

E2. The method of paragraph E1, further comprising: establishing atleast one sliding scale time window between two established timewindows; and running the capacitance resistance modeling for eachestablished sliding time window to generate interwell connectivities foreach well pair, wherein the generated interwell connectivities derivedfrom the sliding time window are included in the solving of thecontinuous function.

E3. The method of paragraph E1, wherein the continuous function is bellshaped, log-normal, betta, logistic curve, or any combination thereof.

E4. The method of paragraph E1, further comprising comparing thegenerated average forecast value of injected fluid for the firstinjection well and a threshold to determine if the first injection wellis a candidate for stimulation.

E5. The method of paragraph E1, further comprising generating an averageforecast value of injected fluid for at least one other injection well.

E6. The method of paragraph E5, further comprising ranking the firstinjection well and the at least one other injection well based on thegenerated average forecast values of injected fluid.

E7. The method of paragraph E1, wherein all of the productions wellscommunication with the first injection well have broken through.

E8. The method of paragraph E1, wherein less than all of the productionswells communication with the first injection well have broken through.

E9. The method of paragraph E1, wherein determining the value ofinjected fluid as a function of time for the first injection wellincludes using an equation, wherein the equation is: VOIF(t)=Σ_(j)^(N)f_(ij)(t)f_(oj)(t) wherein f_(oj)(t) is oil cut as a function oftime and f_(ij)(t) is interwell connectivity as a function of time.

E10. A computing system for analyzing a flood operation on a hydrocarbonreservoir having heavy oil, the system comprising: at least oneprocessor; and at least one memory containing computer executableinstructions, that when executed by the at least one processor, causethe computing system to perform a method comprising: for a firstinjection well of the hydrocarbon reservoir: receiving injection ratedata and production rate data for production wells communicating withthe first injection well for a period of time; establishing a pluralityof time windows based on breakthrough time of each production well thathas broken through until the current date, wherein the production ratedata is indicative of breakthrough; running capacitance resistancemodeling for each established time window to generate interwellconnectivities for each well pair; solving a continuous function foreach well pair using the capacitance resistance modeling generatedinterwell connectivity of the well pair over time to estimate interwellconnectivity for each well pair in the future; using the production ratedata to solve an oil-cut model for each production well that has brokenthrough to estimate oil-cut value for each production well in thefuture; and determining a value of injected fluid for the firstinjection well as a function of time in the future using the continuousfunction estimated interwell connectivity and the estimated oil-cutvalue.

E11. The system of paragraph E10, wherein the computer executableinstructions are further configured to establish at least one slidingscale time window between two established time windows; and run thecapacitance resistance modeling for each established sliding time windowto generate interwell connectivities for each well pair, wherein thegenerated interwell connectivities derived from the sliding time windoware included in the solving of the continuous function.

E12. The system of paragraph E10, wherein the continuous function isbell shaped, log-normal, betta, logistic curve, or any combinationthereof.

E13. The system of paragraph E10, wherein the computer executableinstructions are further configured to compare the generated averageforecast value of injected fluid for the first injection well and athreshold to determine if the first injection well is a candidate forstimulation.

E14. The system of paragraph E10, wherein the computer executableinstructions are further configured to generate an average forecastvalue of injected fluid for at least one other injection well.

E15. The system of paragraph E14, wherein the computer executableinstructions are further configured rank to the first injection well andthe at least one other injection well based on the generated averageforecast values of injected fluid.

E16. The system of paragraph E10, wherein all of the productions wellscommunication with the first injection well have broken through.

E17. The system of paragraph E10, wherein less than all of theproductions wells communication with the first injection well havebroken through.

E18. The system of paragraph E10, wherein determining the value ofinjected fluid as a function of time for the first injection wellincludes using an equation, wherein the equation is: VOIF(t)=Σ_(j)^(N)f_(ij)(t) f_(oj)(t) wherein f_(oj)(t) is oil cut as a function oftime and f_(ij)(t) is interwell connectivity as a function of time.

E19. A computer-readable storage medium comprising computer-executableinstructions which, when executed by a computing system, cause thecomputing system to perform a method of analyzing a flood operation on ahydrocarbon reservoir having heavy oil, the method comprising: for afirst injection well of the hydrocarbon reservoir: receiving injectionrate data and production rate data for production wells communicatingwith the first injection well for a period of time; establishing aplurality of time windows based on breakthrough time of each productionwell that has broken through until the current date, wherein theproduction rate data is indicative of breakthrough; running capacitanceresistance modeling for each established time window to generateinterwell connectivities for each well pair; solving a continuousfunction for each well pair using the capacitance resistance modelinggenerated interwell connectivity of the well pair over time to estimateinterwell connectivity for each well pair in the future; using theproduction rate data to solve an oil-cut model for each production wellthat has broken through to estimate oil-cut value for each productionwell in the future; and determining a value of injected fluid for thefirst injection well as a function of time in the future using thecontinuous function estimated interwell connectivity and the estimatedoil-cut value.

E20. The computer-readable storage medium of paragraph E19, furthercomprising generating an average forecast value of injected fluid for atleast one other injection well; and ranking the first injection well andthe at least one other injection well based on the generated averageforecast values of injected fluid.

VOIF

F1. A computer implemented method of determining a value of injectedfluid for a flood operation on a hydrocarbon reservoir, the methodcomprising: for a first injection well of the hydrocarbon reservoir:receiving injection rate data for the first injection well andproduction rate data for at least one production well communicating withthe injection well; running capacitance resistance modeling using thereceived data to generate an interwell connectivity for each injectionwell and production well pair; determining a value of injected fluid foreach injection well and production well pair using the generatedinterwell connectivity for the well pair and an oil-cut value for theproduction well of the pair; and aggregating the generated values ofinjected fluid per pair to determine a value of injected fluid for thefirst injection well.

F2. The method of paragraph F1, further comprising generating a value ofinjected fluid for at least one other injection well.

F3. The method of paragraph F2, further comprising ranking the firstinjection well and the at least one other injection well based on thevalues of injected fluid.

F4. The method of paragraph F3, further comprising generating a netvalue of injected fluid for the first injection well and the at leastone other injection well.

F5. The method of paragraph F4, further comprising re-ranking the firstinjection well and the at least one other injection well based on thenet values of injected fluid.

F6. The method of paragraph F1, wherein the value of injected fluid forthe first injection well is determined at a steady state.

F7. The method of paragraph F1, wherein the value of injected fluid forthe first injection well is determined as transient.

F8. The method of paragraph F1, wherein the value of injected fluid forthe first injection well is determined using sensitivity analysis.

F9. The method of paragraph F1, wherein the hydrocarbon reservoir mayinclude a plurality of zones, and each zone is treated as an injectionwell.

F10. The method of paragraph F1, further comprising recommending aconformance candidate or a stimulation candidate.

F11. The method of paragraph F1, further comprising recommending atarget injection rate.

F12. A computing system for determining a value of injected fluid for aflood operation on a hydrocarbon reservoir, the system comprising: atleast one processor; and at least one memory containing computerexecutable instructions, that when executed by the at least oneprocessor, cause the computing system to perform a method comprising:for a first injection well of the hydrocarbon reservoir: receivinginjection rate data for the first injection well and production ratedata for at least one production well communicating with the injectionwell; running capacitance resistance modeling using the received data togenerate an interwell connectivity for each injection well andproduction well pair; determining a value of injected fluid for eachinjection well and production well pair using the generated interwellconnectivity for the well pair and an oil-cut value for the productionwell of the pair; and aggregating the generated values of injected fluidper pair to determine a value of injected fluid for the first injectionwell.

F13. The system of paragraph F12, wherein the computer executableinstructions are further configured to generate a value of injectedfluid for at least one other injection well.

F14. The system of paragraph F13, wherein the computer executableinstructions are further configured to rank the first injection well andthe at least one other injection well based on the values of injectedfluid.

F15. The system of paragraph F14, wherein the computer executableinstructions are further configured to generate a net value of injectedfluid for the first injection well and the at least one other injectionwell.

F16. The system of paragraph F15, wherein the computer executableinstructions are further configured to re-rank the first injection welland the at least one other injection well based on the net values ofinjected fluid.

F17. The system of paragraph F12, wherein the value of injected fluidfor the first injection well is determined at a steady state, astransient, using sensitivity analysis, or any combination thereof.

F18. The system of paragraph F12, wherein the hydrocarbon reservoir mayinclude a plurality of zones, and each zone is treated as an injectionwell.

F19. The system of paragraph F12, further comprising recommending aconformance candidate, a stimulation candidate, a target injection rate,or any combination thereof.

F20. A computer-readable storage medium comprising computer-executableinstructions which, when executed by a computing system, cause thecomputing system to perform a method of determining a value of injectedfluid for a flood operation on a hydrocarbon reservoir, the methodcomprising: for a first injection well of the hydrocarbon reservoir:receiving injection rate data for the first injection well andproduction rate data for at least one production well communicating withthe injection well; running capacitance resistance modeling using thereceived data to generate an interwell connectivity for each injectionwell and production well pair; determining a value of injected fluid foreach injection well and production well pair using the generatedinterwell connectivity for the well pair and an oil-cut value for theproduction well of the pair; and aggregating the generated values ofinjected fluid per pair to determine a value of injected fluid for thefirst injection well.

CRM Zonal

G1. A computer implemented method for analyzing a flood operation for ahydrocarbon reservoir having a plurality of zones, the methodcomprising: receiving injection profile data (ILT) and injection rates;using the received injection profile data and injection rates to spliteach injection well into multiple zonal level injectors; and runningcapacitance resistance modeling treating each zonal level injector as asingle injector, wherein running capacitance resistance modelingincludes generating interwell connectivities at the zonal level.

G2. The method of paragraph G1, further comprising determining if thereis crossflow between at least two zones.

G3. The method of paragraph G1, further comprising generating at leastone PLT.

G4. The method of paragraph G1, further comprising generating continuousproduction profile (PLT).

G5. The method of paragraph G4, wherein continuous production profile(PLT) is generated if secondary recovery is contributing in production,which can be obtained from injection rates and zonal levelconnectivities by summing up contribution of zonal injectors and theirconnectivities to at least one of the producers.

G6. The method of paragraph G4, wherein continuous production profilefrom primary and secondary production is obtained by using the primaryand secondary recovery portion of CRM estimate once the CRM zonal isperformed.

G7. The method of paragraph G6, wherein the primary portion ofproduction profile is estimated from CRM zonal level primary portionwhich is an exponential decline.

G8. The method of paragraph G6, wherein the secondary portion ofproduction rates at zonal level are obtained from summing themultiplication of injection rates and connectivities at zonal level fromall injectors contributing in the production of a given producer.

G9. The method of paragraph G1, further comprising interpolating forPLT, ILT, or both.

G10. A computing system for analyzing a flood operation for ahydrocarbon reservoir having a plurality of zones, the systemcomprising: at least one processor; and at least one memory containingcomputer executable instructions, that when executed by the at least oneprocessor, cause the computing system to perform a method comprising:receiving injection profile data (ILT) and injection rates; using thereceived injection profile data and injection rates to split eachinjection well into multiple zonal level injectors; and runningcapacitance resistance modeling treating each zonal level injector as asingle injector, wherein running capacitance resistance modelingincludes generating interwell connectivities at the zonal level.

G11. The system of paragraph G10, wherein the computer executableinstructions are further configured to generate a value of injectedfluid for at least one other injection well.

G12. The system of paragraph G10, wherein the computer executableinstructions are further configured to determine if there is crossflowbetween at least two zones.

G13. The system of paragraph G10, wherein the computer executableinstructions are further configured to generate at least one PLT.

G14. The system of paragraph G10, wherein the computer executableinstructions are further configured to generate continuous productionprofile (PLT).

G15. The system of paragraph G14, wherein the continuous productionprofile (PLT) is generated if secondary recovery is contributing inproduction, which can be obtained from injection rates and zonal levelconnectivities by summing up contribution of zonal injectors and theirconnectivities to at least one of the producers.

G16. The system of paragraph G14, wherein the continuous productionprofile from primary and secondary production is obtained by using theprimary and secondary recovery portion of CRM estimate once the CRMzonal is performed.

G17. The system of paragraph G16, wherein the primary portion ofproduction profile is estimated from CRM zonal level primary portionwhich is an exponential decline,

G18. The system of paragraph G16, wherein the secondary portion ofproduction rates at zonal level are obtained from summing themultiplication of injection rates and connectivities at zonal level fromall injectors contributing in the production of a given producer.

G19. The system of paragraph G10, wherein the computer executableinstructions are further configured to interpolate for PLT, ILT, orboth.

G20. A computer-readable storage medium comprising computer-executableinstructions which, when executed by a computing system, cause thecomputing system to perform a method of analyzing a flood operation fora hydrocarbon reservoir having a plurality of zones, the methodcomprising: receiving injection profile data (ILT) and injection rates;using the received injection profile data and injection rates to spliteach injection well into multiple zonal level injectors; and runningcapacitance resistance modeling treating each zonal level injector as asingle injector, wherein running capacitance resistance modelingincludes generating interwell connectivities at the zonal level.

from first provisional patent application:

H1. A computer implemented method of using Capacitance Resistance Model(CRM) to identify candidate injector wells, the method comprising:receiving injector data for a plurality of injector wells, producer datafor a plurality of producer wells, and flowing pressure data as well asrelative bottomhole location, injection and production profile data,polygons indicating geo-bodies, geological boundaries or structuralboundaries such as fault or fractures from 3D seismic and/or 4D seismicdata; pressure transient testing data including any of pulse test,interwell tracer information indicating well pair in communication;receiving phase data for phases; performing CRM analysis using thereceived data; repeating the CRM analysis; identifying a plurality ofcandidate injector wells from injector well properties including any ofvalue of injection, priorities workover, conformance controlcharacteristics both in areal and vertical sweep efficiency; anddetermining at least a candidate injector well from the plurality ofinjector wells.

H2. The method of paragraph H1, further comprising identifying aplurality of candidate injector wells and a plurality of producer wellsresponsive to connectivity maps.

H3. The method of paragraph H2, wherein the at least a candidateinjector well is determined from the plurality of injector wells and theplurality of producer wells.

H4. The method of paragraph H1, further comprising ranking the pluralityof candidate injector wells.

H5. The method of paragraph H1, wherein performing CRM analysiscomprises performing sensitivity analysis by a) adding noise based onmeasurement accuracy to injection, production and pressure data togenerate many data set, b) representing different earth model byallowing or limiting connectivities compared to a base case model; c)perform sliding time window based analysis to attain interactivity as afunction of time; and d) screening connectivities based on theirconfidence level.

H6. The method of paragraph H1, wherein the connectivity maps indicatehigh connectivity of >0.5, with high confidence as demonstrated bycoefficient of variation less than 10%, and wherein the connectivitiesthat exist are common in all history-matched model with similarhistory-matched error post adding noise to the input data.

H7. The method of paragraph H6, further comprising identifying thecandidate injector well zone, wells and area as targets for conformanceoperations.

H8. The method of paragraph H1, wherein the connectivity maps indicatelow connectivity (>0.25), with high confidence as demonstrated bycoefficient of variation less than 10%, and wherein the connectivitiesthat exist are common in all history-matched model with similarhistory-matched error post adding noise to the input data.

H9. The method of paragraph H8, further comprising identifying thecandidate injector well zone, wells and area as targets for enhanced oilrecovery operations.

H10. The method of paragraph H9, wherein the enhanced oil recoveryoperations include at least one of chemical flooding or polymerflooding.

H11. The method of paragraph H1, wherein the injector data includesinjector profile data over time, wherein the injector profile dataincludes a distribution of injection for each zone.

H12. The method of paragraph H1, wherein the producer data includesproducer profile data over time, wherein the producer profile dataincludes a distribution of production for each zone.

H13. The method of paragraph H1, further comprising receiving at leastone of geological data, tracer data, or pulse test data; and using it inone or more of the analyses.

H14. The method of paragraph H1, wherein the phase data includes atleast one of total injected fluid.

H15. The method of paragraph H14, wherein the injected fluid is selectedfrom any of water, hydrocarbons (gas or oil); CO₂, N₂, and combinationsthereof.

H16. The method of paragraph H15, wherein the injected fluid is injectedin sequence, and alternating between different injection fluids.

H17. The method of paragraph H15, wherein the injected fluid is sourgas.

H18. The method of paragraph H1, further comprising determine aninjection value for all injectors, and wherein the injector value foreach injector indicates the amount of oil being produced in offsetproducers by injecting in the injector.

H19. The method of paragraph H18, wherein the injector value is ameasure of ranking, prioritizing work over, acid jobs, increasinginjection to improve reservoir performance.

H20. The method of paragraph H19, further comprising quantifyingcontribution of injectors as a function of oil production to evaluateexternal factors.

H21. The method of paragraph H20, wherein the external factor is from anaquifer as a driving force.

H22. The method of paragraph H20, further comprising identifying aninjector well with the highest injection value.

H23. A computer implemented method of generating zonal split data, themethod comprising: receiving first profile from either ILT (injectionlogging tools) or PLT (production logging tools) data for a first date;receiving a second ILT/PLT data for a second date; and interpolatingbetween the first PLT data and the second PLT data to dynamicallygenerate zonal split data between the first date and the second date.

H24. A computer implemented method of generating zonal split data, themethod comprising: receiving first ILT data for a first date; receivingsecond ILT data for a second date; and interpolating between the firstILT data and the second ILT data to dynamically generate zonal splitdata between the first date and the second date.

H25. The method of paragraph H24, further comprising generating acontinuous production profile based on interwell connectivity modelobtained using CRM at zonal level.

H26. A method performing flooding analysis, the method comprising usinga combination of CRM, a zonal splits generator, and a streamgrid.

H27. A method performing flooding analysis, the method comprising: usinginterpreted seismic data (3D or 4D) to condition CRM connectivity modelto the more likely scenario of the earth model and potentialcommunicating well pairs indicating location of geological or pressurebarriers.

H28. The method of paragraph H27, further comprising identifyinginterwell cumulative injected fluid agreement with saturation changefrom 4D seismic data during history matching.

H29. A method performing flooding analysis, the method comprisingdynamically generating and redefining patterns for a plurality ofinjector wells and a plurality of producer wells.

H30. The method of paragraph H29, further comprising using at least oneof Delaunay triangulation or Voronoi.

from second provisional patent application:

I1. A computer implemented method of analyzing a flooding operation fora subterranean hydrocarbon bearing reservoir, the method comprising:establishing a dataset of allowed well connections for a plurality ofinjection wells and production well of the subterranean reservoir;iteratively modifying the dataset of allowed well connections based on adistance category, a static features category, a dynamic featurescategory, or any combination thereof, wherein the static featurescategory uses static data of the subterranean reservoir and the dynamicfeatures category uses dynamic data of the subterranean reservoir; andafter iteratively modifying the dataset of allowed well connections,running capacitance resistive modeling (CRM) using the modified datasetof allowed well connections as an input.

I2. The method of paragraph I1, wherein iteratively modifying thedataset of allowed well connections according to a priority, wherein thepriority includes using an order, wherein the order is the distancecategory first, the static features category second, and the dynamicfeatures category third.

I3. The method of paragraph I1, wherein iteratively modifying thedataset of allowed well connections according to a priority, wherein thepriority includes using an order, wherein the order is the dynamicfeatures category first, the static features second, and the distancecategory third.

I4. The method of paragraph I1, wherein there is a priority within thedistance category.

I5. The method of paragraph I1, wherein there is a priority within thestatic features category.

I6. The method of paragraph I1, wherein there is a priority within thedynamic features category.

I7. The method of paragraph I1, wherein the static data includes atleast one of geological data, seismic data, three dimensional (3D)seismic data, faults, fractures, geobodies, or a processed version ofany of these.

I8. The method of paragraph I1, wherein the dynamic data includes atleast one of well production data, injection rate data, pressure data,location data, fluid production geochemistry data, tracer data, logdata, production log data, well log data, well test data, time lapseseismic data, four dimensional (4D) seismic data, maps of change inpressure and saturation, maps of pressure and saturation, preliminarywell connections, pressure transient, pulse test, a processed version ofany of these, or any combination thereof.

I9. The method of paragraph I1, wherein running capacitance resistivemodeling (CRM) is constrained by at least one geological data, seismicdata, three dimensional (3D) seismic data, faults, fractures, geobodies,well production data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, a processed version of anyof these, or any combination thereof.

I10. The method of paragraph I1, further comprising updating at leastone application or component based on output from the running of CRM.

I11. The method of paragraph I1, wherein modifying the dataset ofallowed well connections includes modifying a value of an allowed wellconnection in response to receiving user input indicating that the valueshould be changed.

I12. A computer system for analyzing a flooding operation for asubterranean hydrocarbon bearing reservoir, the system comprising: atleast one processor; and at least one memory containing computerexecutable instructions, that when executed by the at least oneprocessor, cause the computing system to perform the method of paragraphI1.

I13. A computer program product including a non-transitorycomputer-readable medium having computer-readable code on it, thecomputer readable code being configured to implement the method ofparagraph I1.

I14. A computer implemented method of analyzing a flooding operation fora subterranean hydrocarbon bearing reservoir, the method comprising:determining a dataset of allowed well connections for a plurality ofinjection wells and production well of the subterranean reservoir basedon a distance category; determining a dataset of allowed wellconnections for a plurality of injection wells and production well ofthe subterranean reservoir based on a static features category, whereinthe static features category uses static data of the subterraneanreservoir; determining a dataset of allowed well connections for aplurality of injection wells and production well of the subterraneanreservoir based on a static features category, wherein the dynamicfeatures category uses dynamic data of the subterranean reservoir;comparing the determined well connections; combining the determined wellconnections based on the comparisons and a priority to determine allowedwell connections; and running capacitance resistive modeling (CRM) usingthe allowed well connections as an input.

I15. The method of paragraph I14, wherein the priority includes thedynamic features category having the highest priority, the staticfeatures category having the next highest priority, and the distancecategory having the next highest priority.

I16. The method of paragraph I14, wherein there is a priority within thedistance category.

I17. The method of paragraph I14, wherein there is a priority within thestatic features category.

I18. The method of paragraph I14, wherein there is a priority within thedynamic features category.

I19. The method of paragraph I14, wherein the static data includes atleast one of geological data, seismic data, three dimensional (3D)seismic data, faults, fractures, geobodies, or a processed version ofany of these.

I20. The method of paragraph I14, wherein the dynamic data includes atleast one of well production data, injection rate data, pressure data,location data, fluid production geochemistry data, tracer data, logdata, production log data, well log data, well test data, time lapseseismic data, four dimensional (4D) seismic data, maps of change inpressure and saturation, maps of pressure and saturation, preliminarywell connections, pressure transient, pulse test, a processed version ofany of these, or any combination thereof.

I21. The method of paragraph I14, wherein running capacitance resistivemodeling (CRM) is constrained by at least one geological data, seismicdata, three dimensional (3D) seismic data, faults, fractures, geobodies,well production data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, a processed version of anyof these, or any combination thereof.

I22. The method of paragraph I14, further comprising updating at leastone application or component based on output from the running of CRM.

I23. The method of paragraph I14, further comprising modifying a valueof an allowed well connection in response to receiving user inputindicating that the value should be changed.

I24. A computer system for analyzing a flooding operation for asubterranean hydrocarbon bearing reservoir, the system comprising: atleast one processor; and at least one memory containing computerexecutable instructions, that when executed by the at least oneprocessor, cause the computing system to perform the method of paragraphI14.

I25. A computer program product including a non-transitorycomputer-readable medium having computer-readable code on it, thecomputer readable code being configured to implement the method ofparagraph I14.

I26. A computer implemented method of analyzing a flooding operation fora subterranean hydrocarbon bearing reservoir, the method comprising:receiving field data for the subterranean reservoir; processing thefield data for the subterranean reservoir; determining allowed wellconnections; and running capacitance resistive modeling (CRM) using theallowed well connections as an input.

I27. The method of paragraph I26, wherein determining allowed wellconnections includes using at least one of geological data, seismicdata, three dimensional (3D) seismic data, faults, fractures, geobodies,well production data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, a processed version of anyof these, or any combination thereof.

I28. The method of paragraph I26, wherein running capacitance resistivemodeling (CRM) is constrained by at least one geological data, seismicdata, three dimensional (3D) seismic data, faults, fractures, geobodies,well production data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, a processed version of anyof these, or any combination thereof.

I29. The method of paragraph I26, further comprising updating at leastone application or component based on output from the running of CRM.

I30. A computer system for analyzing a flooding operation for asubterranean hydrocarbon bearing reservoir, the system comprising: atleast one processor;

and at least one memory containing computer executable instructions,that when executed by the at least one processor, cause the computingsystem to perform the method of paragraph I26.

I31. A computer program product including a non-transitorycomputer-readable medium having computer-readable code on it, thecomputer readable code being configured to implement the method ofparagraph I26.

I32. A method of analyzing a flooding operation for a subterraneanhydrocarbon bearing reservoir, the method comprising: injecting materialinto the reservoir for the flooding operation; determining allowed wellconnections from data from the reservoir, wherein the allowed wellconnections are input to capacitance resistive modeling (CRM); andmaking at least one decision that is likely to improve the floodingoperation based on the output of CRM.

I33. The method of paragraph I32, wherein the at least one decision isrelated to optimization, history matching, conformance control, changinga parameter of the flooding operation, or any combination thereof.

I34. The method of paragraph I32, wherein determining the allowed wellconnections includes using at least one of geological data, seismicdata, three dimensional (3D) seismic data, faults, fractures, geobodies,well production data, injection rate data, pressure data, location data,fluid production geochemistry data, tracer data, log data, productionlog data, well log data, well test data, time lapse seismic data, fourdimensional (4D) seismic data, maps of change in pressure andsaturation, maps of pressure and saturation, preliminary wellconnections, pressure transient, pulse test, a processed version of anyof these, or any combination thereof.

I35. The method of paragraph I32, wherein determining the allowed wellconnections includes: establishing a dataset of allowed well connectionsfor a plurality of injection wells and production well of thesubterranean reservoir; modifying the dataset of allowed wellconnections based on a distance category, a static features category, adynamic features category, or any combination thereof, wherein thestatic features category uses static data of the subterranean reservoirand the dynamic features category uses dynamic data of the subterraneanreservoir, wherein the modified dataset provides the allowed wellconnections that are input to capacitance resistive modeling (CRM).

I36. The method of paragraph I32, wherein determining the allowed wellconnections includes: determining a dataset of allowed well connectionsfor a plurality of injection wells and production well of thesubterranean reservoir based on a distance category; determining adataset of allowed well connections for a plurality of injection wellsand production well of the subterranean reservoir based on a staticfeatures category, wherein the static features category uses static dataof the subterranean reservoir; determining a dataset of allowed wellconnections for a plurality of injection wells and production well ofthe subterranean reservoir based on a static features category, whereinthe dynamic features category uses dynamic data of the subterraneanreservoir; comparing the determined well connections; and combining thedetermined well connections based on the comparisons and a priority todetermine allowed well connections.

What is claimed is:
 1. A computer implemented method of determining aconformance control treatment for a well of a hydrocarbon reservoir,wherein the well is in fluidic communication with a plurality of zonesof the hydrocarbon reservoir, the method comprising: receiving data foreach zone of the plurality of zones, the received data to be used todetermine a residence time distribution for each zone; determining theresidence time distribution for each zone of the plurality of zonesusing the received data; identifying at least one zone of the pluralityof zones to be treated with a conformance agent by comparing thedetermined residence time distributions of the zones and a residencetime distribution threshold; and recommending a first conformancecontrol treatment at breakthrough time of the slowest identified zonefor the at least one identified zone.
 2. The method of claim 1, furthercomprising recommending a slug size for the first conformance controltreatment, wherein the slug size is determined by calculating injectedvolume for a period of time which is not more than 50% of a breakthroughtime for the fastest identified zone.
 3. The method of claim 1, furthercomprising recommending a concentration of the conformance agent for thefirst conformance control treatment, wherein the concentration of theconformance agent is based on a resistance factor of the conformanceagent and a rheology of the conformance agent.
 4. The method of claim 3,wherein the resistance factor of the conformance agent is sufficient toreduce original velocity of the fastest identified zone by a factor ofabout
 10. 5. The method of claim 1, further comprising: receivingupdated data for each zone of the plurality of zones after the firstconformance control treatment; determining an updated residence timedistribution for each zone of the plurality of zone after the firstconformance control treatment; identifying at least one zone of theplurality of zones after the first conformance control treatment to betreated with a conformance agent in a second conformance controltreatment based on a comparison of the updated residence timedistribution of each zone and the residence time distribution threshold;and recommending a second conformance control treatment at an updatedbreakthrough time of the slowest identified zone after the firstconformance control treatment for the at least one identified zone afterthe first conformance control treatment.
 6. The method of claim 5,further comprising recommending a slug size for the second conformancecontrol treatment, wherein the slug size for the second conformancecontrol treatment is determined by calculating injected volume for aperiod of time which is not more than 50% of an updated breakthroughtime for the fastest identified zone.
 7. The method of claim 5, furthercomprising recommending a concentration of the conformance agent for thesecond conformance control treatment, wherein the concentration of theconformance agent for the second conformance control treatment is basedon a resistance factor of the conformance agent for the secondconformance control treatment and a rheology of the conformance agentfor the second conformance control treatment.
 8. The method of claim 7,wherein the resistance factor of the conformance agent of the secondconformance control treatment is sufficient to reduce updated velocityof the fastest identified zone after the first conformance controltreatment by a factor of about 10.